2023

International Brain, L.a.b.o.r.a.t.o.r.y., Bonacchi, N., Chapuis, G.A., Churchland, A.K., DeWitt, E.E.J., Faulkner, M., ...Laranjeira, I.C. (2023). A modular architecture for organizing, processing and sharing neurophysiology data.. Nature Methods, doi:10.1038/s41592-022-01742-6
Jo, R., Gretton, A. (2023). A Neural Mean Embedding Approach for Back-door and Front-door Adjustment.
Flesch, T., Saxe, A., Summerfield, C. (2023). Continual task learning in natural and artificial agents. Trends in Neurosciences, doi:10.1016/j.tins.2022.12.006
Schrab, A., Kim, I., Guedj, B., Gretton, A. (2023). Efficient Aggregated Kernel Tests using Incomplete U-statistics.
Pogodin, R., Deka, N., Li, Y., Sutherland, D.J., Veitch, V., Gretton, A. (2023). Efficient Conditionally Invariant Representation Learning.
Mastrogiuseppe, F., Hiratani, N., Latham, P. (2023). Evolution of neural activity in circuits bridging sensory and abstract knowledge. eLife, 12 doi:10.7554/eLife.79908
Schrab, A., Guedj, B., Gretton, A. (2023). KSD Aggregated Goodness-of-fit Test.
Flesch, T., Nagy, D.G., Saxe, A., Summerfield, C. (2023). Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. PLoS Computational Biology, 19 (1), doi:10.1371/journal.pcbi.1010808
Nelli, S., Braun, L., Dumbalska, T., Saxe, A., Summerfield, C. (2023). Neural knowledge assembly in humans and neural networks. Neuron, doi:10.1016/j.neuron.2023.02.014
Hiratani, N., Mehta, Y., Lillicrap, T., Latham, P. (2023). On the Stability and Scalability of Node Perturbation Learning.
Li, Z., Meunier, D., Mollenhauer, M., Gretton, A. (2023). Optimal Rates for Regularized Conditional Mean Embedding Learning.
Galgali, A.R., Sahani, M., Mante, V. (2023). Residual dynamics resolves recurrent contributions to neural computation. Nature Neuroscience, doi:10.1038/s41593-022-01230-2
Anastasiou, A., Barp, A., Briol, F.-.X., Ebner, B., Gaunt, R.E., Ghaderinezhad, F., ...Liu, Q. (2023). Stein's method meets computational statistics: A review of some recent developments. Statistical Science,
Masís, J., Chapman, T., Rhee, J.Y., Cox, D.D., Saxe, A.M. (2023). Strategically managing learning during perceptual decision making. eLife, 12 doi:10.7554/eLife.64978
Yu, C., Soulat, H., Burgess, N., Sahani, M. (2023). Structured recognition for generative models with explaining away.
Walker, W., Soulat, H., Yu, C., Sahani, M. (2023). Unsupervised representation learning with recognition-parametrised probabilistic models.
Walker, W., Soulat, H., Yu, C., Sahani, M. (2023). Unsupervised representation learning with recognition-parametrised probabilistic models.

2022

Moskovitz, T., Miller, K., Sahani, M., Botvinick, M.M. (2022). A Unified Theory of Dual-Process Control.
Alabdulmohsin, I., Chiou, N., D'Amour, A., Gretton, A., Koyejo, S., Kusner, M.J., ...Tsai, K. (2022). Adapting to Latent Subgroup Shifts via Concepts and Proxies.
Saglietti, L., Mannelli, S.S., Saxe, A.M. (2022). An Analytical Theory of Curriculum Learning in Teacher-Student Networks.
Saglietti, L., Mannelli, S.S., Saxe, A. (2022). An analytical theory of curriculum learning in teacher–student networks. Journal of Statistical Mechanics: Theory and Experiment, 2022 doi:10.1088/1742-5468/ac9b3c
Saglietti, L., Mannelli, S.S., Saxe, A. (2022). An analytical theory of curriculum learning in teacher–student networks. Journal of Statistical Mechanics: Theory and Experiment, 2022 doi:10.1088/1742-5468/ac9b3c
Zhu, Y., Gultchin, L., Gretton, A., Kusner, M., Silva, R. (2022). Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach.
Flesch, T., Saxe, A., Summerfield, C. (2022). Continual task learning in natural and artificial agents.
Kanagawa, H., Gretton, A., Mackey, L. (2022). Controlling Moments with Kernel Stein Discrepancies.
Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., ...Morimoto, J. (2022). Deep learning, reinforcement learning, and world models. Neural Networks, doi:10.1016/j.neunet.2022.03.037
Hiratani, N., Latham, P.E. (2022). Developmental and evolutionary constraints on olfactory circuit selection. Proceedings of the National Academy of Sciences of the United States of America, 119 (11), doi:10.1073/pnas.2100600119
Schrab, A., Kim, I., Guedj, B., Gretton, A. (2022). Efficient Aggregated Kernel Tests using Incomplete U-statistics. .
Pogodin, R., Deka, N., Li, Y., Sutherland, D.J., Veitch, V., Gretton, A. (2022). Efficient Conditionally Invariant Representation Learning.
Braun, L., Dominé, C.C.J., Fitzgerald, J.E., Saxe, A.M. (2022). Exact learning dynamics of deep linear networks with prior knowledge.
Braun, L., Dominé, C.C.J., Fitzgerald, J.E., Saxe, A.M. (2022). Exact learning dynamics of deep linear networks with prior knowledge.
Boboeva, V., Pezzotta, A., Clopath, C., Akrami, A. (2022). From recency to central tendency biases in working memory: a unifying network model. .
Jo, R., Chen, Y., Doucet, A., Gretton, A. (2022). Importance Weighting Approach in Kernel Bayes' Rule.
Sarao Mannelli, S., Gerace, F., Rostamzadeh, N., Saglietti, L. (2022). Inducing bias is simpler than you think.
Singh, A.K., Ding, D., Saxe, A., Hill, F., Lampinen, A.K. (2022). Know your audience: specializing grounded language models with the game of Dixit.
Salmasi, M., Sahani, M. (2022). Learning neural codes for perceptual uncertainty.
Salmasi, M., Sahani, M. (2022). Learning neural codes for perceptual uncertainty.
Chadwick, A., Khan, A.G., Poort, J., Blot, A., Hofer, S.B., Mrsic-Flogel, T.D., Sahani, M. (2022). Learning shapes cortical dynamics to enhance integration of relevant sensory input. Neuron, doi:10.1016/j.neuron.2022.10.001
Lee, S., Mannelli, S.S., Clopath, C., Goldt, S., Saxe, A.M. (2022). Maslow’s Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation.
Lee, S., Mannelli, S.S., Clopath, C., Goldt, S., Saxe, A.M. (2022). Maslow’s Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation.
Glaser, P., Arbel, M., Doucet, A., Gretton, A. (2022). Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference.
Flesch, T., Nagy, D.G., Saxe, A., Summerfield, C. (2022). Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.
Chen, Y., Xu, L., Gulcehre, C., Le Paine, T., Gretton, A., de Freitas, N., Doucet, A. (2022). On Instrumental Variable Regression for Deep Offline Policy Evaluation. Journal of Machine Learning Research, 23 (302), 1-40.
Pezzotta, A., Briscoe, J. (2022). Optimal control of gene regulatory networks for morphogen-driven tissue patterning.
Flesch, T., Juechems, K., Dumbalska, T., Saxe, A., Summerfield, C. (2022). Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron, 110 (24), 4212-4219. doi:10.1016/j.neuron.2022.12.004
Flesch, T., Juechems, K., Dumbalska, T., Saxe, A., Summerfield, C. (2022). Orthogonal representations for robust context-dependent task performance in brains and neural networks.. Neuron, doi:10.1016/j.neuron.2022.01.005
Gerace, F., Saglietti, L., Mannelli, S.S., Saxe, A., Zdeborova, L. (2022). Probing transfer learning with a model of synthetic correlated datasets. Machine Learning: Science and Technology, 3 (1), doi:10.1088/2632-2153/ac4f3f
Gerace, F., Saglietti, L., Mannelli, S.S., Saxe, A., Zdeborova, L. (2022). Probing transfer learning with a model of synthetic correlated datasets. Machine Learning: Science and Technology, 3 (1), doi:10.1088/2632-2153/ac4f3f
Tootoonian, S., Schaefer, A.T., Latham, P.E. (2022). Sparse connectivity for MAP inference in linear models using sister mitral cells. PLOS Computational Biology, 18 (1), doi:10.1371/journal.pcbi.1009808
Yu, C., Soulat, H., Burgess, N., Sahani, M. (2022). Structured Recognition for Generative Models with Explaining Away.
Saxe, A.M., Sodhani, S., Lewallen, S. (2022). The Neural Race Reduction: Dynamics of Abstraction in Gated Networks.
Saxe, A.M., Sodhani, S., Lewallen, S. (2022). The Neural Race Reduction: Dynamics of Abstraction in Gated Networks.
Jarvis, D., Klein, R., Rosman, B., Saxe, A.M. (2022). The Role of Learning Regime, Architecture and Dataset Structure on Systematic Generalization in Simple Neural Networks.
Castillo, I., Orbanz, P. (2022). Uniform estimation in stochastic block models is slow. Electronic Journal of Statistics, 16 (1), 2947-3000. doi:10.1214/22-EJS2014
Walker, W.I., Soulat, H., Yu, C., Sahani, M. (2022). Unsupervised representational learning with recognition-parametrised probabilistic models.

2021

Fernández, T., Gretton, A., Rindt, D., Sejdinovic, D. (2021). A Kernel Log-Rank Test of Independence for Right-Censored Data. Journal of the American Statistical Association, doi:10.1080/01621459.2021.1961784
Bicknell, B.A., Häusser, M. (2021). A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron, doi:10.1016/j.neuron.2021.09.044
Marx, A., Gretton, A., Mooij, J.M. (2021). A Weaker Faithfulness Assumption based on Triple Interactions.
Saglietti, L., Mannelli, S.S., Saxe, A. (2021). An Analytical Theory of Curriculum Learning in Teacher-Student Networks.
Sarao Mannelli, S., Urbani, P. (2021). Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems.
Lee, S., Goldt, S., Saxe, A. (2021). Continual Learning in the Teacher-Student Setup: Impact of Task Similarity.
Lee, S., Goldt, S., Saxe, A. (2021). Continual Learning in the Teacher-Student Setup: Impact of Task Similarity.
Amjad, J., Lyu, Z., Rodrigues, M.R.D. (2021). Deep learning model-aware regulatization with applications to Inverse Problems. IEEE Transactions on Signal Processing, doi:10.1109/TSP.2021.3125601
Jo, R., Kanagawa, H., Gretton, A. (2021). Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation.
Trautmann, E.M., O'Shea, D.J., Sun, X., Marshel, J.H., Crow, A., Hsueh, B., ...Allen, W. (2021). Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nature Communications, 12 (1), doi:10.1038/s41467-021-23884-5
Duncker, L., Sahani, M. (2021). Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings.. Current Opinion in Neurobiology, 70 163-170. doi:10.1016/j.conb.2021.10.014
Baker, A., Biazzo, I., Braunstein, A., Catania, G., Dall’Asta, L., Ingrosso, A., ...Muntoni, A.P. (2021). Epidemic mitigation by statistical inference from contact tracing data. Proceedings of the National Academy of Sciences, 118 (32), doi:10.1073/pnas.2106548118
Boboeva, V., Pezzotta, A., Clopath, C. (2021). Free recall scaling laws and short-term memory effects in a latching attractor network.. Proceedings of the National Academy of Sciences of USA, 118 (49), doi:10.1073/pnas.2026092118
Saxe, A., Nelli, S., Summerfield, C. (2021). If deep learning is the answer, what is the question?. Nature Reviews Neuroscience, 22 (1), 55-67. doi:10.1038/s41583-020-00395-8
Juechems, K., Saxe, A. (2021). Inferring Actions, Intentions, and Causal Relations in a Deep Neural Network.
Glaser, P., Arbel, M., Gretton, A. (2021). KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support.
Poort, J., Wilmes, K.A., Blot, A., Chadwick, A., Sahani, M., Clopath, C., ...Khan, A.G. (2021). Learning and attention increase visual response selectivity through distinct mechanisms. Neuron, doi:10.1016/j.neuron.2021.11.016
Nelli, S., Braun, L., Dumbalska, T., Saxe, A., Summerfield, C. (2021). Neural knowledge assembly in humans and deep networks.
Sun, W., Advani, M., Spruston, N., Saxe*, A., Fitzgerald*, J.E. (2021). Organizing memories for generalization in complementary learning systems.
Duncker, L., Driscoll, L., Shenoy, K., Sahani, M., Susillo, D. (2021). Organizing recurrent network dynamics by task-computation to enable continual learning.
Schwarz, J., Jayakumar, S.M., Pascanu, R., Latham, P.E., Teh, Y.W. (2021). Powerpropagation: A sparsity inducing weight reparameterisation.
Soulat, H., Keshavarzi, S., Margrie, T.W., Sahani, M. (2021). Probabilistic Tensor Decomposition of Neural Population Spiking Activity.
Gerace, F., Saglietti, L., Mannelli, S.S., Saxe, A., Zdeborová, L. (2021). Probing transfer learning with a model of synthetic correlated datasets.
Mastouri, A., Zhu, Y., Gultchin, L., Korba, A., Silva, R., Kusner, M., ...Muandet, K. (2021). Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction.
Amjad, J., Lyu, Z., Rodrigues, M.R.D. (2021). Regression with Deep Neural Networks: Generalization Error Guarantees, Learning Algorithms, and Regularizers.
Flesch, T., Juechems, K., Dumbalska, T., Saxe, A., Summerfield, C. (2021). Rich and lazy learning of task representations in brains and neural networks.
Li, Y., Pogodin, R., Sutherland, D.J., Gretton, A. (2021). Self-Supervised Learning with Kernel Dependence Maximization.
Gothner, T., Gonçalves, P.J., Sahani, M., Linden, J.F., Hildebrandt, K.J. (2021). Sustained Activation of PV+ Interneurons in Core Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli.. Cereb Cortex, doi:10.1093/cercor/bhaa347
Aitchison, L., Jegminat, J., Menendez, J.A., Pfister, J.-.P., Pouget, A., Latham, P.E. (2021). Synaptic plasticity as Bayesian inference.. Nat Neurosci, doi:10.1038/s41593-021-00809-5
Pogodin, R., Mehta, Y., Lillicrap, T.P., Latham, P.E. (2021). Towards Biologically Plausible Convolutional Networks.

2020

Rosca, M., Weber, T., Gretton, A., Mohamed, S. (2020). A case for new neural network smoothness constraints.
Fernández, T., Xu, W., Ditzhaus, M., Gretton, A. (2020). A kernel test for quasi-independence.
Korba, A., Salim, A., Arbel, M., Luise, G., Gretton, A. (2020). A Non-Asymptotic Analysis for Stein Variational Gradient Descent.
Wenliang, L.K., Moskovitz, T., Kanagawa, H., Sahani, M. (2020). Amortised learning by wake-sleep.
Cao, Y., Summerfield, C., Saxe, A. (2020). Characterizing emergent representations in a space of candidate learning rules for deep networks.
Sarao Mannelli, S., Biroli, G., Cammarota, C., Krzakala, F., Urbani, P., Zdeborova, L. (2020). Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval.
Korshunova, I., Gal, Y., Gretton, A., Dambre, J. (2020). Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing, doi:10.1016/j.neucom.2019.11.108
Hiratani, N., Latham, P. (2020). Developmental and evolutionary constraints on olfactory circuit selection. .
Goldt, S., Advani, M.S., Saxe, A.M., Krzakala, F., Zdeborová, L. (2020). Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup. Journal of Statistical Mechanics: Theory and Experiment, 2020 124010. doi:10.1088/1742-5468/abc61e
Najafi, F., Elsayed, G.F., Cao, R., Pnevmatikakis, E., Latham, P.E., Cunningham, J.P., Churchland, A.K. (2020). Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning. Neuron, doi:10.1016/j.neuron.2019.09.045
Boboeva, V., Pezzotta, A., Clopath, C. (2020). Free recall scaling laws and short-term memory effects in a latching attractor network.
Advani, M.S., Saxe, A.M., Sompolinsky, H. (2020). High-dimensional dynamics of generalization error in neural networks. Neural Networks, 132 428-446. doi:10.1016/j.neunet.2020.08.022
Pogodin, R., Latham, P.E. (2020). Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks.
Fernandez Aguilar, T., Gretton, A., Rivera, N., XU, W. (2020). Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data.
Liu, F., Xu, W., Lu, J., Zhang, G., Gretton, A., Sutherland, D.J. (2020). Learning deep kernels for non-parametric two-sample tests.
Mannelli, S.S., Biroli, G., Cammarota, C., Krzakala, F., Urbani, P., Zdeborova, L. (2020). Marvels and Pitfalls of the Langevin Algorithm in Noisy High-Dimensional Inference. PHYSICAL REVIEW X, 10 (1), doi:10.1103/PhysRevX.10.011057
Amjad, J., Lyu, Z., Rodrigues, M.R.D. (2020). Model-Aware Regularization For Learning Approaches To Inverse Problems.
Nishiyama, Y., Kanagawa, M., Gretton, A., Fukumizu, K. (2020). Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models. Machine Learning, doi:10.1007/s10994-019-05852-9
Jerjian, S.J., Sahani, M., Kraskov, A. (2020). Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons.. Elife, 9 doi:10.7554/eLife.54139
Rusakov, D.A., Savtchenko, L.P., Latham, P.E. (2020). Noisy Synaptic Conductance: Bug or a Feature?. Trends in Neurosciences, doi:10.1016/j.tins.2020.03.009
Rutten, V., Bernacchia, A., Sahani, M., Hennequin, G. (2020). Non-reversible Gaussian processes for identifying latent dynamical structure in neural data.
Rutten, V., Bernacchia, A., Sahani, M., Hennequin, G. (2020). Non-reversible Gaussian processes for identifying latent dynamical structure in neural data.
Musslick, S., Saxe, A., Hoskin, A.N., Reichman, D., Cohen, J.D. (2020). On the Rational Boundedness of Cognitive Control: Shared Versus Separated Representations. PsyArXiv .
Sarao Mannelli, S., Vanden-Eijnden, E., Zdeborova, L. (2020). Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions.
Lauwers, E., Lalli, G., Brandner, S., Collinge, J., Compernolle, V., Duyckaerts, C., ...Helmy, A. (2020). Potential human transmission of amyloid β pathology: surveillance and risks. The Lancet Neurology, 19 (10), 872-878. doi:10.1016/S1474-4422(20)30238-6
Hiratani, N., Latham, P.E. (2020). Rapid Bayesian learning in the mammalian olfactory system. Nature Communications, 11 (1), doi:10.1038/s41467-020-17490-0
Masis, J.A., Chapman, T., Rhee, J.Y., Cox, D.D., Saxe, A.M. (2020). Rats strategically manage learning during perceptual decision making.
Mannelli, S.S., Zdeborova, L. (2020). Thresholds of descending algorithms in inference problems. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2020 (3), doi:10.1088/1742-5468/ab7123

2019

Richards, B.A., Lillicrap, T.P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ...Ganguli, S. (2019). A deep learning framework for neuroscience.. Nat Neurosci, 22 (11), 1761-1770. doi:10.1038/s41593-019-0520-2
Richards, B.A., Lillicrap, T.P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., ...Ganguli, S. (2019). A deep learning framework for neuroscience.. Nat Neurosci, 22 (11), 1761-1770. doi:10.1038/s41593-019-0520-2
Saxe, A.M., McClelland, J.L., Ganguli, S. (2019). A mathematical theory of semantic development in deep neural networks. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 116 (23), 11537-11546. doi:10.1073/pnas.1820226116
Fernández, T., Gretton, A. (2019). A maximum-mean-discrepancy goodness-of-fit test for censored data.
Li, W., Sahani, M. (2019). A neurally plausible model for online recognition and postdiction.
Vertes, E., Sahani, M. (2019). A neurally plausible model learns successor representations in partially observable environments.
Lomelí, M., Rowland, M., Gretton, A., Ghahramani, Z. (2019). Antithetic and Monte Carlo kernel estimators for partial rankings. Statistics and Computing, doi:10.1007/s11222-019-09859-z
Greenberg, D.S., Nonnenmacher, M., Macke, J.H. (2019). Automatic Posterior Transformation for Likelihood-Free Inference.
Korshunova, I., Gal, Y., Gretton, A., Dambre, J. (2019). Conditional BRUNO: A neural process for exchangeable labelled data.
Williamson, R.S., Sahani, M., Pillow, J.W. (2019). Correction: The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction (Plos Comput Biol, (2015), 11, 4, 10.1371/journal.pcbi.1004141). PLoS Computational Biology, 15 (6), doi:10.1371/journal.pcbi.1007139
Amjad, J., Lyu, Z., Rodrigues, M.R.D. (2019). Deep Learning for Inverse Problems: Bounds and Regularizers.
Camerlenghi, F., Lijoi, A., Orbanz, P., Prunster, I. (2019). Distribution theory for hierarchical processes. Annals of Statistics, 47 (1), 67-92. doi:10.1214/17-AOS1678
Goldt, S., Advani, M.S., Saxe, A.M., Krzakala, F., Zdeborová, L. (2019). Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.
Goldt, S., Advani, M.S., Saxe, A.M., Krzakala, F., Zdeborová, L. (2019). Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.
Jonathan, P.i.l.l.o.w., Sahani, M. (2019). Editorial overview: Machine learning, big data, and neuroscience. Current Opinion in Neurobiology, 55 iii-iv. doi:10.1016/j.conb.2019.05.002
Veitch, V., Austern, M., Zhou, W., Blei, D.M., Orbanz, P. (2019). Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data.
Dai, B., Liu, Z., Dai, H., He, N., Gretton, A., Le, S., Schurmaans, D. (2019). Exponential Family Estimation via Adversarial Dynamics Embedding.
Goldt, S., Advani, M.S., Saxe, A.M., Krzakala, F., Zdeborová, L. (2019). Generalisation dynamics of online learning in over-parameterised neural networks. doi:10.48550/ARXIV.1901.09085
Goldt, S., Advani, M.S., Saxe, A.M., Krzakala, F., Zdeborová, L. (2019). Generalisation dynamics of online learning in over-parameterised neural networks.
Dai, B., Dai, H., Gretton, A., Song, L., Schuurmans, D., He, N. (2019). Kernel Exponential Family Estimation via Doubly Dual Embedding.
Singh, R., Sahani, M., Gretton, A. (2019). Kernel Instrumental Variable Regression.
Singh, R., Sahani, M., Gretton, A. (2019). Kernel Instrumental Variable Regression.
Wenliang, L.K., Sutherland, D.J., Strathmann, H., Gretton, A. (2019). Learning deep kernels for exponential family densities.
Duncker, L., Bohner, G., Boussard, J., Sahani, M. (2019). Learning interpretable continuous-time models of latent stochastic dynamical systems.
Arbel, M., Korba, A., Salim, A., Gretton, A. (2019). Maximum Mean Discrepancy Gradient Flow.
Zhou, W., Veitch, V., Austern, M., Adams, R.P., Orbanz, P. (2019). Non-vacuous generalization bounds at the Im-AGeNet scale: A Pac-Bayesian compression approach.
Saxe, A.M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B.D., Cox, D.D. (2019). On the information bottleneck theory of deep learning. Journal of Statistical Mechanics: Theory and Experiment, 124020. doi:10.1088/1742-5468/ab3985
Mannelli, S.S., Krzakala, F., Urbani, P., Zdeborova, L. (2019). Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models.
Lieder, I., Adam, V., Frenkel, O., Jaffe-Dax, S., Sahani, M., Ahissar, M. (2019). Perceptual bias reveals slow-updating in autism and fast-forgetting in dyslexia.. Nat Neurosci, 22 (2), 256-264. doi:10.1038/s41593-018-0308-9
Salmasi, M., Loebel, A., Glasauer, S., Stemmler, M. (2019). Short-term synaptic depression can increase the rate of information transfer at a release site. PLOS COMPUTATIONAL BIOLOGY, 15 (1), doi:10.1371/journal.pcbi.1006666
Montijn, J., Liu, R., Aschner, A., Kohn, A., Latham, P., Pouget, A. (2019). Strong information-limiting correlations in early visual areas. .
Salmasi, M., Stemmler, M., Glasauer, S., Loebel, A. (2019). Synaptic Information Transmission in a Two-State Model of Short-Term Facilitation. ENTROPY, 21 (8), doi:10.3390/e21080756
Currin, C.B., Khoza, P.N., Antrobus, A.D., Latham, P.E., Vogels, T.P., Raimondo, J.V. (2019). Think: Theory for Africa.. PLoS Comput Biol, 15 (7), e1007049. doi:10.1371/journal.pcbi.1007049
Rapela, J., Proix, T., Todorov, D., Truccolo, W. (2019). Uncovering low-dimensional structure in high-dimensional representations of long-term recordings in people with epilepsy.
Sarao Mannelli, S., Biroli, G., Cammarota, C., Krzakala, F., Zdeborova, L. (2019). Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models.

2018

Meyer, A.F., Poort, J., O'Keefe, J., Sahani, M., Linden, J.F. (2018). A Head-Mounted Camera System Integrates Detailed Behavioral Monitoring with Multichannel Electrophysiology in Freely Moving Mice. Neuron, doi:10.1016/j.neuron.2018.09.020
Saxe, A.M., McClelland, J.L., Ganguli, S. (2018). A mathematical theory of semantic development in deep neural networks.
Rapela, J., Westerfield, M., Townsend, J. (2018). A New Foreperiod Effect on Intertrial Phase Coherence. Part I: Existence and Behavioral Relevance. NEURAL COMPUTATION, 30 (9), 2348-2383. doi:10.1162/neco_a_01109
Saxe*, A.M., Advani*, M. (2018). A theory of memory replay and generalization performance in neural networks.
Nye, M., Saxe, A. (2018). Are Efficient Deep Representations Learnable?.
Korshunova, I., Degrave, J., Huszár, F., Gal, Y., Gretton, A., Dambre, J. (2018). Bruno: A deep recurrent model for exchangeable data.
Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A. (2018). Demystifying MMD GANs.
Khan, A.G., Poort, J., Chadwick, A., Blot, A., Sahani, M., Mrsic-Flogel, T.D., Hofer, S.B. (2018). Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex. Nature Neuroscience, 1-9. doi:10.1038/s41593-018-0143-z
Sutherland, D.J., Strathmann, H., Arbel, M., Gretton, A. (2018). Efficient and principled score estimation with Nyström kernel exponential families.
Zhang, Y., Saxe, A.M., Advani, M.S., Lee, A.A. (2018). Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning. Molecular Physics, 1-10. doi:10.1080/00268976.2018.1483535
Zhang, Y., Saxe, A.M., Advani, M.S., Lee, A.A. (2018). Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning.
Adorisio, M., Pezzotta, A., de Mulatier, C., Micheletti, C., Celani, A. (2018). Exact and Efficient Sampling of Conditioned Walks. Journal of Statistical Physics, 170 (1), 79-100. doi:10.1007/s10955-017-1911-y
Vértes, E., Sahani, M. (2018). Flexible and accurate inference and learning for deep generative models.
Earle, A.C., Saxe, A.M., Rosman, B. (2018). Hierarchical Subtask Discovery with Non-Negative Matrix Factorization.
Jitkrittum, W., Sangkloy, P., Schölkopf, B., Kanagawa, H., Hays, J., Gretton, A. (2018). Informative features for model comparison.
Arbel, M., Gretton, A.L. (2018). Kernel Conditional Exponential Family.
Austern, M., Orbanz, P. (2018). Limit theorems for invariant distributions.
Bansal, Y., Advani, M., Cox, D.D., Saxe, A.M. (2018). Minnorm training: an algorithm for training over-parameterized deep neural networks. arXiv:1806.00730,
Amjad, J., Sokolic, J., Rodrigues, M.R.D. (2018). On Deep Learning for Inverse Problems.
Arbel, M., Sutherland, D.J., Binkowski, M., Gretton, A. (2018). On gradient regularizers for MMD GANs.
Saxe, A.M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B.D., Cox, D.D. (2018). On the Information Bottleneck Theory of Deep Learning.
Bloem-Reddy, B., Orbanz, P. (2018). Random-walk models of network formation and sequential Monte Carlo methods for graphs. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 80 (5), 871-898. doi:10.1111/rssb.12289
Masís, J., Saxe, A.M., Cox, D.D. (2018). Rats optimize reward rate and learning speed in a 2-AFC task.
Duncker, L., Sahani, M. (2018). Temporal alignment and latent Gaussian process factor inference in population spike trains.
Rapela, J. (2018). Traveling waves appear and disappear in unison with produced speech.

2017

Jitkrittum, W., Xu, W., Szabó, Z., Fukumizu, K., Gretton, A. (2017). A linear-time kernel goodness-of-fit test.
Douglas, L., Zarov, I., Gourgoulias, K., Lucas, C., Hart, C., Baker, A., ...Johri, S. (2017). A Universal Marginalizer for Amortized Inference in Generative Models.
Jitkrittum, W., s.z.a.b.o., g.r.e.t.t.o.n. (2017). An Adaptive Test of Independence with Analytic Kernel Embeddings.
Abbott, L.F., Angelaki, D.E., Carandini, M., Churchland, A.K., Dan, Y., Dayan, P., ...Harris, K.D. (2017). An International Laboratory for Systems and Computational Neuroscience. Neuron, 96 (6), 1213-1218. doi:10.1016/j.neuron.2017.12.013
Orbanz, P., Teh, Y.W. (2017). Bayesian Nonparametric Models. In Encyclopedia of Machine Learning and Data Mining. (pp. 107-116). Springer US.
Bang, D., Aitchison, L., Moran, R., Herce Castanon, S., Rafiee, B., Mahmoodi, A., ...Summerfield, C. (2017). Confidence matching in group decision-making. Nature Human Behaviour, 1 (6), 0117. doi:10.1038/s41562-017-0117
Latham, P.E. (2017). Correlations demystified. NATURE NEUROSCIENCE, 20 (1), 6-8.
Panzeri, S., Harvey, C.D., Piasini, E., Latham, P.E., Fellin, T. (2017). Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. NEURON, 93 (3), 491-507. doi:10.1016/j.neuron.2016.12.036
Sriperumbudur, B., Fukumizu, K., Gretton, A., Hyvärinen, A., Kumar, R. (2017). Density Estimation in Infinite Dimensional Exponential Families. Journal of Machine Learning Research,
Sutherland, D.J., Tung, H.-.Y., Strathmann, H., De, S., Ramdas, A., Smola, A., Gretton, A. (2017). Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.
Shelton, J., Gasthaus, J., Z, D., Luecke, J., Gretton, A. (2017). GP-select: Accelerating EM using adaptive subspace preselection. Neural Computation, doi:10.1162/neco_a_00982
Earle, A.C., Saxe, A.M., Rosman, B. (2017). Hierarchical Subtask Discovery With Non-Negative Matrix Factorization. Workshop on Lifelong Learning: A Reinforcement Learning Approach at ICML,
Saxe, A.M., Earle, A.C., Rosman, B. (2017). Hierarchy through composition with multitask LMDPS.
Saxe, A.M., Earle, A.C., Rosman, B. (2017). Hierarchy Through Composition with Multitask LMDPs.
Advani, M.S., Saxe, A.M. (2017). High-dimensional dynamics of generalization error in neural networks.
James, L.F., Orbanz, P. (2017). Independence by Random Scaling.
Salmasi, M., Stemmler, M., Glasauer, S., Loebel, A. (2017). Information Rate Analysis of a Synaptic Release Site Using a Two-State Model of Short-Term Depression. NEURAL COMPUTATION, 29 (6), 1528-1560. doi:10.1162/NECO_a_00962
Meyer, A.F., Williamson, R.S., Linden, J.F., Sahani, M. (2017). Models of neuronal stimulus-response functions: Elaboration, estimation, and evaluation. Frontiers in Systems Neuroscience, 10 doi:10.3389/fnsys.2016.00109
Musslick, S., Saxe, A.M., Ozcimder, K., Dey, B., Henselman, G., Cohen, J.D. (2017). Multitasking Capability Versus Learning Efficiency in Neural Network Architectures.
Bloem-Reddy, B., Orbanz, P. (2017). Preferential Attachment and Vertex Arrival Times.
Chambers, C., Akram, S., Adam, V., Pelofi, C., Sahani, M., Shamma, S., Pressnitzer, D. (2017). Prior context in audition informs binding and shapes simple features. NATURE COMMUNICATIONS, 8 doi:10.1038/ncomms15027
Rapela, J. (2017). Rhythmic production of consonant-vowel syllables synchronizes traveling waves in speech-processing brain regions.
Zylberberg, J., Pouget, A., Latham, P.E., Shea-Brown, E. (2017). Robust information propagation through noisy neural circuits. PLOS COMPUTATIONAL BIOLOGY, 13 (4), doi:10.1371/journal.pcbi.1005497
Nonnenmacher, M., Behrens, C., Berens, P., Bethge, M., Macke, J.H. (2017). Signatures of criticality arise from random subsampling in simple population models (vol 13, e100571, 2017). PLOS COMPUTATIONAL BIOLOGY, 13 (12), doi:10.1371/journal.pcbi.1005886
Orbanz, P. (2017). Subsampling large graphs and invariance in networks.
Navajas, J., Hindocha, C., Foda, H., Keramati, M., Latham, P.E., Bahrami, B. (2017). The idiosyncratic nature of confidence. Nature Human Behaviour, doi:10.1038/s41562-017-0215-1
Hildebrandt, K.J., Sahani, M., Linden, J.F. (2017). The Impact of Anesthetic State on Spike-Sorting Success in the Cortex: A Comparison of Ketamine and Urethane Anesthesia. FRONTIERS IN NEURAL CIRCUITS, 11 doi:10.3389/fncir.2017.00095

2016

McClelland, J.L., Sadeghi, Z., Saxe, A.M. (2016). A Critique of Pure Hierarchy: Uncovering Cross-Cutting Structure in a Natural Dataset. Neurocomputational Models of Cognitive Development and Processing, 51-68.
Rubenstein, P., Chwialkowski, K., Gretton, A. (2016). A Kernel Test for Three-Variable Interactions with Random Processes.
Chwialkowski, K., Strathmann, H., Gretton, A. (2016). A Kernel Test of Goodness of Fit.
Grabska-Barwińska, A., Barthelmé, S., Beck, J., Mainen, Z.F., Pouget, A., Latham, P.E. (2016). A probabilistic approach to demixing odors. Nature Neuroscience, doi:10.1038/nn.4444
Bounliphone, W., Belilovsky, E., Blaschko, M.B., Antonoglou, I., Gretton, A. (2016). A test of relative similarity for model selection in generative models.
W, B., Belilovsky, E., Blaschko, M., Antonoglou, I., Gretton, A. (2016). A Test of Relative Similarity For Model Selection in Generative Models.
Baldassano*, C., Saxe*, A.M. (2016). A theory of learning dynamics in perceptual decision-making.
Orbanz, P., Szegedy, B. (2016). Borel liftings of graph limits. ELECTRONIC COMMUNICATIONS IN PROBABILITY, 21 doi:10.1214/16-ECP14
Bohner, G., Sahani, M. (2016). CONVOLUTIONAL HIGHER ORDER MATCHING PURSUIT.
Jitkrittum, W., Szabo, Z., Chwialkowski, K., Gretton, A. (2016). Distinguishing distributions with interpretable features.
Jitkrittum, W., Szabo, Z., Chwialkowski, K., Gretton, A. (2016). Distinguishing distributions with interpretable features.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2016). Distribution Regression with Minimax-Optimal Guarantee.
Rapela, J. (2016). Entrainment of traveling waves to rhythmic motor acts.
Bounliphone, W., Belilovsky, E., Tenenhaus, A., Antonoglou, I., Gretton, A., Blashcko, M.B. (2016). Fast Non-Parametric Tests of Relative Dependency and Similarity.
Kanagawa, M., Nishiyama, Y., Gretton, A., Fukumizu, K. (2016). Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Computation, 28 382-444. doi:10.1162/NECO_a_00806
Salmasi, M., Buettner, U., Glasauer, S. (2016). Fractal dimension analysis for spike detection in low SNR extracellular signals. JOURNAL OF NEURAL ENGINEERING, 13 (3), doi:10.1088/1741-2560/13/3/036004
Strathmann, H., Sejdinovic, D., Livingston, S., Szabo, Z., Gretton, A. (2016). Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
Juechems, K., Saxe, A.M. (2016). Inferring Actions, Intentions, and Causal Relations in a Neural Network.
Stringer, C., Pachitariu, M., Steinmetz, N.A., Okun, M., Bartho, P., Harris, K.D., ...Lesica, N.A. (2016). Inhibitory control of correlated intrinsic variability in cortical networks.. Elife, 5 doi:10.7554/eLife.19695
Williamson, R.S., Ahrens, M.B., Linden, J.F., Sahani, M. (2016). Input-Specific Gain Modulation by Local Sensory Context Shapes Cortical and Thalamic Responses to Complex Sounds. Neuron, doi:10.1016/j.neuron.2016.05.041
Jitkrittum, W., Szabo, Z., Chwialkowski, K., Gretton, A. (2016). Interpretable Distribution Features with Maximum Testing Power. .
Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schoelkopf, B. (2016). Kernel Mean Shrinkage Estimators. Journal of Machine Learning Research, 17 1-41.
Strathmann, H., Sejdinovic, D., Livingston, S., Schuster, I., Lomeli Garcia, M., Szabo, Z., ...Gretton, A. (2016). Kernel techniques for adaptive Monte Carlo methods.
Zhang, Q., Filippi, S., Gretton, A., Sejdinovic, D. (2016). Large-Scale Kernel Methods for Independence Testing.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2016). Learning Theory for Distribution Regression. .
Weichwald, S., Grosse-Wentrup, M., Gretton, A. (2016). MERLiN: Mixture Effect Recovery in Linear Networks. IEEE Journal of Selected Topics in Signal Processing, doi:10.1109/JSTSP.2016.2601144
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2016). Minimax-optimal distribution regression.
Saxe, A.M., Norman, K. (2016). Optimal storage capacity associative memories exhibit retrieval-induced forgetting.
Navajas, J., Bahrami, B., Latham, P.E. (2016). Post-decisional accounts of biases in confidence. Current Opinion in Behavioral Sciences, 11 55-60. doi:10.1016/j.cobeha.2016.05.005
Weichwald, S., Gretton, A., Schölkopf, B., Grosse-Wentrup, M. (2016). Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data.
Adam, V., Hensman, J., Sahani, M. (2016). Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference.
Sahani, M., Bohner, G., Meyer, A. (2016). SCORE-MATCHING ESTIMATORS FOR CONTINUOUS-TIME POINT-PROCESS REGRESSION MODELS.
Ferrè, E.R., Sahani, M., Haggard, P. (2016). Subliminal stimulation and somatosensory signal detection.. Acta psychologica, 170 103-111.
Tsai*, C.Y., Saxe*, A., Cox, D. (2016). Tensor Switching Networks.
Tsai, C.-.Y., Saxe, A., Cox, D. (2016). Tensor Switching Networks.
O'Shea, D.J., Trautmann, E., Chandrasekaran, C., Stavisky, S., Kao, J., Sahani, M., ...Shenoy, K.V. (2016). The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces.. Experimental neurology, doi:10.1016/j.expneurol.2016.08.003
McClelland, J.L., Hansen, S., Saxe, A. (2016). Tutorial Workshop on Contemporary Deep Neural Network Models.
Aitchison, L., Corradi, N., Latham, P.E. (2016). Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables.. PLoS Comput Biol, 12 (12), e1005110. doi:10.1371/journal.pcbi.1005110

2015

Saxe, A.M. (2015). A deep learning theory of perceptual learning dynamics.
Bounliphone, W., Gretton, A., Tenenhaus, A., Blaschko, M. (2015). A low variance consistent test of relative dependency.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). A Simple and Consistent Technique for Vector-valued Distribution Regression.
Gretton, A. (2015). A simpler condition for consistency of a kernel independence test. ArXiv e-prints .
Hildebrandt, K.J., Goncalves, P.J., Sahani, M., Linden, J.F. (2015). Activation of parvalbumin-positive interneurons enhances transient responses and changes tuning of offset responses in awake auditory cortex..
Goncalves, P.J., Linden, J.F., Sahani, M. (2015). Augmentation of A1 responses is reproduced by a recurrent circuit model combining strong excitation with synaptic depression..
Park, M., Jitkrittum, W., Qamar, A., Szabo, Z., Buesing, L., Sahani, M. (2015). Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM).
Orbanz, P., Roy, D.M. (2015). Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 37 (2), 437-461. doi:10.1109/TPAMI.2014.2334607
Stringer, C., Patchitariu, M., Hildebrandt, K.J., Bartho, P., Harris, K.D., Linden, J.F., ...Sahani, M. (2015). Changes in inhibition explain variability in cortical activity and its role in sensory representations..
Stringer, C., Patchitariu, M., Hildebrandt, K.J., Bartho, P., Harris, K.D., Linden, J.F., ...Sahani, M. (2015). Changes in inhibition explain variability in cortical activity and its role in sensory representations..
Szabo, Z., Gretton, A., Póczos, B., Sriperumbudur, B. (2015). Consistent Vector-valued Distribution Regression.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Consistent Vector-valued Regression on Probability Measures.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Distribution Regression: Computational and Statistical Tradeoffs.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Distribution Regression: Computational and Statistical Tradeoffs.
Aitchison, L., Bang, D., Bahrami, B., Latham, P.E. (2015). Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.. PLoS Computational Biology, 11 (10), doi:10.1371/journal.pcbi.1004519
Macke, J.H., Buesing, L., Sahani, M. (2015). Estimating state and model parameters in state-space models of spike trains. In Chen, Z. (Ed.), Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press.
Schulz, D.P.A., Sahani, M., Carandini, M. (2015). Five key factors determining pairwise correlations in visual cortex. JOURNAL OF NEUROPHYSIOLOGY, 114 (2), 1022-1033. doi:10.1152/jn.00094.2015
Strathmann, H., Sejdinovic, D., Livingston, S., Szabo, Z., Gretton, A. (2015). Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
Jitkrittum, W., Gretton, A., Heess, N., Eslami, A., Lakshminarayanan, B., Sejdinovic, D., Szabo, Z. (2015). Just-In-Time Kernel Regression for Expectation Propagation.
Jitkrittum, W., Gretton, A., Heess, N., Eslami, A., Lakshminarayanan, B., Sejdinovic, D., Szabo, Z. (2015). Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Jitkrittum, W., Gretton, A., Heess, N., Eslami, A., Lakshminarayanan, B., Sejdinovic, D., Szabo, Z. (2015). Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Poort, J., Khan, A.G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., ...Mrsic-Flogel, T.D. (2015). Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex.. Neuron, 86 1478-1490. doi:10.1016/j.neuron.2015.05.037
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Learning Theory for Vector-Valued Distribution Regression.
Meyer, A.F., Linden, J.F., Sahani, M. (2015). Local sensory context modulates responses to complex sounds in multiple brain areas along the auditory pathway..
Rapela, J., Kostuk, M., Rowat, P.F., Mullen, T., Chang, E.F., Bouchard, K. (2015). Modeling neural activity at the ensemble level.
Linden, J.F., Hildebrandt, K.J., Sahani, M. (2015). Optogenetic modulation of transient and sustained responses to tones in auditory cortex of awake mice..
Jitkrittum, W., Gretton, A., Heess, N. (2015). Passing Expectation Propagation Messages with Kernel Methods. .
Goodfellow, I.J., Vinyals, O., Saxe, A.M. (2015). Qualitatively Characterizing Neural Network Optimization Problems.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Regression on Probability Measures: A Simple and Consistent Algorithm.
James, L.F., Orbanz, P., Teh, Y.W. (2015). Scaled subordinators and generalizations of the Indian buffet process.
Pachitariu, M., Lyamzin, D.R., Sahani, M., Lesica, N.A. (2015). State-dependent population coding in primary auditory cortex.. J Neurosci, 35 (5), 2058-2073. doi:10.1523/JNEUROSCI.3318-14.2015
Lee, R., Saxe, A.M. (2015). The Effect of Pooling in a Deep Learning Model of Perceptual Learning.
Williamson, R.S., Sahani, M., Pillow, J.W. (2015). The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.. PLOS COMPUTATIONAL BIOLOGY, 11 (4), doi:10.1371/journal.pcbi.1004141
Lin, I.C., Okun, M., Carandini, M., Harris, K.D. (2015). The Nature of Shared Cortical Variability. Neuron, 87 (3), 644-656. doi:10.1016/j.neuron.2015.06.035
Szabo, Z., Gretton, A., Póczos, B., Sriperumbudur, B. (2015). Two-stage Sampled Learning Theory on Distributions.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Vector-valued Distribution Regression - Keep It Simple and Consistent.

2014

Chvvialkowski, K., Gretton, A. (2014). A kernel independence test for random processes.
Chwialkowski, K., Gretton, A. (2014). A Kernel Independence Test for Random Processes.
Chwialkowski, K., Gretton, A. (2014). A Kernel Independence Test for Random Processes.
Chwialkowski, K., Sejdinovic, D., Gretton, A. (2014). A Wild Bootstrap for Degenerate Kernel Tests.
Szabo, Z., Gretton, A., Póczos, B., Sriperumbudur, B. (2014). Consistent Distribution Regression via Mean Embedding.
Szabo, Z., Gretton, A., Póczos, B., Sriperumbudur, B. (2014). Distribution Regression - the Set Kernel Heuristic is Consistent.
Bang, D., Fusaroli, R., Tylén, K., Olsen, K., Latham, P.E., Lau, J.Y.F., ...Bahrami, B. (2014). Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making.. Conscious Cogn, 26C 13-23. doi:10.1016/j.concog.2014.02.002
Henniges, M., Turner, R.E., Sahani, M., Eggert, J., Luecke, J. (2014). Efficient Occlusive Components Analysis. Journal of Machine Learning Research, 15 2689-2722.
Saxe, A.M., McClelland, J.L., Ganguli, S. (2014). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.
Grabska-Barwińska, A., Latham, P.E. (2014). How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?. Journal of Computational Neuroscience, 36 (3), 469-481. doi:10.1007/s10827-013-0481-5
Moreno-Bote, R., Beck, J., Kanitscheider, I., Pitkow, X., Latham, P., Pouget, A. (2014). Information-limiting correlations. Nature Neuroscience, 17 (10), 1410-1417. doi:10.1038/nn.3807
Sejdinovic, D., Strathmann, H., Garcia, M.L., Andrieu, C., Gretton, A. (2014). Kernel Adaptive Metropolis-Hastings.
Sejdinovic, D., Strathmann, H., Garcia, M.L., Andrieu, C., Gretton, A. (2014). Kernel Adaptive Metropolis-Hastings.
Muandet, K., Fukumizu, K., Sriperumbudur, B., Gretton, A., Schölkopf, B. (2014). Kernel mean estimation and stein effect.
Muandet, K., Fukumizu, K., Sriperumbudur, B., Gretton, A., Schölkopf, B. (2014). Kernel Mean Estimation and Stein's Effect.
Szabo, Z., Gretton, A., Póczos, B., Sriperumbudur, B. (2014). Learning on Distributions.
Szabo, Z., Gretton, A., Poczos, B., Sriperumbudur, B. (2014). Learning on Distributions.
Lee, R., Saxe, A.M., McClelland, J. (2014). Modeling Perceptual Learning with Deep Networks.
Kanagawa, M., Nishiyama, Y., Gretton, A., Fukumizu, K. (2014). Monte Carlo Filtering Using Kernel Embedding of Distributions.
Saxe, A.M. (2014). Multitask Model-free Reinforcement Learning.
Gallistel, C.R., Krishan, M., Liu, Y., Miller, R., Latham, P.E. (2014). The perception of probability.. PSYCHOLOGICAL REVIEW, 121 (1), 96-123. doi:10.1037/a0035232
Turner, R.E., Sahani, M. (2014). Time-Frequency Analysis as Probabilistic Inference. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 62 (23), 6171-6183. doi:10.1109/TSP.2014.2362100
Szabo, Z., Gretton, A., Poczos, B., Sriperumbudur, B. (2014). Vector-valued Distribution Regression: A Simple and Consistent Approach.
Saxe, A.M. (2014). Workshop on Deep Learning and the Brain.

2013

Sejdinovic, D., Gretton, A., Bergsma, W. (2013). A Kernel Test for Three-Variable Interactions.
Saxe, A.M., McClelland, J.L., Ganguli, S. (2013). A Mathematical Theory of Semantic Development.
Zaremba, W., Gretton, A., Blaschko, M. (2013). B-test: A Non-parametric, Low Variance Kernel Two-sample Test.
Zaremba, W., Gretton, A., Blaschko, M. (2013). B-tests: Low Variance Kernel Two-Sample Tests.
Shenoy, K.V., Sahani, M., Churchland, M.M. (2013). Cortical control of arm movements: a dynamical systems perspective.. ANNUAL REVIEW OF NEUROSCIENCE, VOL 36, 36 337-359. doi:10.1146/annurev-neuro-062111-150509
Saxe, A.M., McClelland, J.L., Ganguli, S. (2013). Dynamics of learning in deep linear neural networks.
Sejdinovic, D., Sriperumbudur, B.K., Gretton, A., Fukumizu, K. (2013). Equivalence of distance-based and RKHS-based statistics in hypothesis testing. Annals of Statistics, 41 2263-2702.
Macke, J.H., Murray, I., Latham, P.E. (2013). Estimation Bias in Maximum Entropy Models. ENTROPY, 15 (8), 3109-3129. doi:10.3390/e15083109
Saxe, A.M., McClelland, J.L., Ganguli, S. (2013). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.
Pachitariu, M., Petitt, N., Dagleish, H., Packer, A., Haüsser, M., Sahani, M. (2013). Extracting regions of interest from biological images with convolutional sparse block coding. Advances in Neural Information Processing, 26
Boots, B., Gordon, G., Gretton, A. (2013). Hilbert Space Embeddings of Predictive State Representations.
Fukumizu, K., Song, L., Gretton, A. (2013). Kernel Bayes’ Rule: Bayesian Inference with Positive Definite Kernels. Journal of Machine Learning Research, 14 3753-3783.
Song, L., Fukumizu, K., Gretton, A. (2013). Kernel Embeddings of Conditional Distributions. IEEE SIGNAL PROCESSING MAGAZINE, 30 (4), 98-111. doi:10.1109/MSP.2013.2252713
Saxe, A.M., McClelland, J.L., Ganguli, S. (2013). Learning hierarchical category structure in deep neural networks.
Rathore, S., Bush, D., Latham, P., Burgess, N. (2013). Oscillatory dynamics in an attractor neural network with firing rate adaptation.
Garrido, M.I., Sahani, M., Dolan, R.J. (2013). Outlier responses reflect sensitivity to statistical structure in the human brain.. PLoS Computational Biology, 9 (3), doi:10.1371/journal.pcbi.1002999
Pouget, A., Beck, J.M., Ma, W.J., Latham, P.E. (2013). Probabilistic brains: knowns and unknowns.. NATURE NEUROSCIENCE, 16 (9), 1170-1178. doi:10.1038/nn.3495
Sahani, M., Williamson, R.S., Ahrens, M.B., Linden, J.F. (2013). Probabilistic methods for linear and multilinear models.. In Depireux, D.A., Elhilali, M. (Eds.), Handbook of Modern Techniques in Auditory Cortex. (pp. 193-222). Nova Science Pub Incorporated.
Wallace, E., Maei, H.R., Latham, P.E. (2013). Randomly Connected Networks Have Short Temporal Memory. NEURAL COMPUTATION, 25 (6), 1408-1439. doi:10.1162/NECO_a_00449
Pachitariu, M., Petreska, B., Sahani, M. (2013). Recurrent linear models of simultaneously-recorded neural populations.
Pachitariu, M., Sahani, M. (2013). Regularization and nonlinearities for neural language models: when are they needed?.
Grünewälder, S., Gretton, A., Shawe-Taylor, J. (2013). Smooth operators. 30th International Conference on Machine Learning, ICML 2013, (PART 3), 2221-2229.
Blaschko, M.B., Zaremba, W., Gretton, A. (2013). Taxonomic prediction with tree-structured covariances.

2012

Gretton, A., Borgwardt, K., Rasch, M., Schoelkopf, B., Smola, A. (2012). A Kernel Two-Sample Test. JMLR, 13 723-773.
Hildebrandt, K.J., Anderson, L.A., Sahani, M., Linden, J.F. (2012). Anaesthetic condition alters response time-course, long-range coordination of firing, and spike isolation quality in mouse auditory cortex..
Rapela, J., Lin, T.-.Y., Westerfield, M., Jung, T.-.P., Townsend, J. (2012). Assisting autistic children with wireless EOG technology.
Whiteley, L., Sahani, M. (2012). Attention in a bayesian framework.. FRONTIERS IN HUMAN NEUROSCIENCE, 6 doi:10.3389/fnhum.2012.00100
Rapela, J., Gramann, K., Westerfield, M., Townsend, J., Makeig, S. (2012). Brain oscillations in Switching vs. Focusing audio-visual attention.
(2012). Brain oscillations in switching vs. focusing audio-visual attention..
Grünewälder, S., Lever, G., Baldassarre, L., Patterson, S., Gretton, A., Pontil, M. (2012). Conditional mean embeddings as regressors. Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2 1823-1830.
Grünewälder, S., Lever, G., Baldassarre, L., Patterson, S., Gretton, A., Pontil, M. (2012). Conditional mean embeddings as regressors - supplementary. Ithaca, NY, USA: ArXiv.
Turner, R.E., Sahani, M. (2012). Decomposing Signals into a Sum of Amplitude and Frequency Modulated Sinusoids using Probabilistic Inference.
Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K. (2012). Feature Selection via Dependence Maximization. JOURNAL OF MACHINE LEARNING RESEARCH, 13 1393-1434.
Garrido, M.I., Barnes, G.R., Sahani, M., Dolan, R.J. (2012). Functional evidence for a dual route to amygdala. Current Biology, 22 (2), 129-134. doi:10.1016/j.cub.2011.11.056
Garrido, M.I., Barnes, G.R., Sahani, M., Dolan, R.J. (2012). Functional evidence for a dual route to amygdala.. CURRENT BIOLOGY, 22 (2), 129-134. doi:10.1016/j.cub.2011.11.056
Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K. (2012). Hilbert Space Embeddings of POMDPs.
Sejdinovic, D., Gretton, A., Sriperumbudur, B., Fukumizu, K. (2012). Hypothesis testing using pairwise distances and associated kernels (with Appendix). .
Lin, I.-.C., Xing, D., Shapley, R. (2012). Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity. JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 33 (3), 559-572. doi:10.1007/s10827-012-0401-0
Buesing, L., Macke, J.H., Sahani, M. (2012). Learning stable, regularised latent models of neural population dynamics.. Network: Computation in Neural Systems, 23 24–47.
Pachitariu, M., Sahani, M. (2012). Learning visual motion in recurrent neural networks.
Grunewalder, S., Lever, G., Baldassarre, L., Pontil, M., Gretton, A. (2012). Modelling transition dynamics in MDPs with RKHS embeddings.
Amjad, J., Uppal, M., Qaisar, S. (2012). Multi-level compress and forward coding for half-duplex relays.
Beck, J.M., Ma, W.J., Pitkow, X., Latham, P.E., Pouget, A. (2012). Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron, 74 30-39.
Sriperumbudur, B.K., Fukumizu, K., Gretton, A., Schoelkopf, B., Lanckriet, G.R.G. (2012). On the empirical estimation of integral probability metrics. ELECTRONIC JOURNAL OF STATISTICS, 6 1550-1599. doi:10.1214/12-EJS722
Gretton, A., Sriperumbudur, B., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K. (2012). Optimal kernel choice for large-scale two-sample tests.
Abid, H., Raja, H., Munir, A., Amjad, J., Mazhar, A., Lee, D.-.Y. (2012). Performance Analysis of WiMAX Best Effort and ertPS Service Classes for Video Transmission..
Teh, Y.W., Titterington, D.M. (2012). Preface..
Lloyd, J.R., Orbanz, P., Ghahramani, Z., Roy, D.M. (2012). Random function priors for exchangeable arrays with applications to graphs and relational data.
Buesing, L., Macke, J.H., Sahani, M. (2012). Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.
Lin, I.-.C., Seitsonen, A.P., Tavernelli, I., Rothlisberger, U. (2012). Structure and Dynamics of Liquid Water from ab Initio Molecular Dynamics-Comparison of BLYP, PBE, and revPBE Density Functionals with and without van der Waals Corrections. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 8 (10), 3902-3910. doi:10.1021/ct3001848
Mysore, G., Sahani, M. (2012). Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation.

2011

Shenoy, K.V., Kaufman, M.T., Sahani, M., Churchland, M.M. (2011). A dynamical systems view of motor preparation: implications for neural prosthetic system design.. ENHANCING PERFORMANCE FOR ACTION AND PERCEPTION: MULTISENSORY INTEGRATION, NEUROPLASTICITY AND NEUROPROSTHETICS, PT II, 192 33-58. doi:10.1016/B978-0-444-53355-5.00003-8
Balci, F., Simen, P., Niyogi, R., Saxe, A., Hughes, J.A., Holmes, P., Cohen, J.D. (2011). Acquisition of decision making criteria: reward rate ultimately beats accuracy. ATTENTION PERCEPTION & PSYCHOPHYSICS, 73 (2), 640-657. doi:10.3758/s13414-010-0049-7
Turner, R.E., Sahani, M. (2011). Demodulation as Probabilistic Inference. IEEE Transactions on Audio, Speech and Language Processing, 19 (8), 2398-2411. doi:10.1109/TASL.2011.2135852
Christianson, G.B., Sahani, M., Linden, J.F. (2011). Depth-dependent temporal response properties in core auditory cortex.. JOURNAL OF NEUROSCIENCE, 31 (36), 12837-12848. doi:10.1523/JNEUROSCI.2863-11.2011
Blundell, C., Teh, Y.W., Heller, K.A. (2011). Discovering non-binary hierarchical structures with Bayesian rose trees. In Mengersen, K., Robert, C.P., Titterington, M. (Eds.), Mixture: Estimation and Applications. (pp. 161-187). Chichester, UK: John Wiley & Sons.
Petreska, B., Yu, B.M., Cunningham, J.P., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2011). Dynamical segmentation of single trials from population neural data.
Macke, J.H., Büsing, L., Cunningham, J.P., Yu, B.M., Shenoy, K.V., Sahani, M. (2011). Empirical models of spiking in neural populations.
Fukumizu, K., Song, L., Gretton, A. (2011). Kernel Bayes’ Rule.
Song, L., Gretton, A., Bickson, D., Low, Y., Guestrin, C. (2011). Kernel Belief Propagation.
Beck, J.M., Latham, P.E., Pouget, A. (2011). Marginalization in neural circuits with divisive normalization.. JOURNAL OF NEUROSCIENCE, 31 (43), 15310-15319. doi:10.1523/JNEUROSCI.1706-11.2011
Silva, R.B.D.A.E., Blundell, C., Teh, Y.W. (2011). Mixed Cumulative Distribution Networks. .
Saxe, A.M., Bhand, M., Mudur, R., Suresh, B., Ng, A.Y. (2011). Modeling Cortical Representational Plasticity With Unsupervised Feature Learning.
Grunewalder, S., Lever, G., Baldassarre, L., Pontil, M., Gretton, A. (2011). Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions. .
Sahani, M., Whiteley, L. (2011). Modelling Cue Integration in Cluttered Environments. In Landy, M., Körding, K., Trommershäuser, J. (Eds.), Sensory Cue Integration. Oxford University Press.
Ahrens, M.B., Sahani, M. (2011). Observers exploit stochastic models of sensory change to help judge the passage of time.. CURRENT BIOLOGY, 21 (3), 200-206. doi:10.1016/j.cub.2010.12.043
Gonzalez, J., Low, Y., Gretton, A., Guestrin, C. (2011). Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees.
Turner, R.E., Sahani, M. (2011). Probabilistic amplitude and frequency demodulation.
Orbanz, P. (2011). Projective limit random probabilities on Polish spaces. ELECTRONIC JOURNAL OF STATISTICS, 5 1354-1373. doi:10.1214/11-EJS641
Blaschko, M., Shelton, J., Bartels, A., Lampert, C., Gretton, A. (2011). Semi-supervised Kernel Canonical Correlation Analysis with Application to Human fMRI. Pattern Recognition Letters, 32 1572-1583.
Afshar, A., Santhanam, G., Yu, B.M., Ryu, S.I., Sahani, M., Shenoy, K.V. (2011). Single-trial neural correlates of arm movement preparation.. NEURON, 71 (3), 555-564. doi:10.1016/j.neuron.2011.05.047
Garrido, M.I., Dolan, R.J., Sahani, M. (2011). Surprise leads to noisier perceptual decisions. i-Perception, 2 (2), 112-120. doi:10.1068/i0411
Garrido, M.I., Dolan, R.J., Sahani, M. (2011). Surprise leads to noisier perceptual decisions. i-Perception, 2 (2), 112-120. doi:10.1068/i0411
Turner, R.E., Sahani, M. (2011). Two problems with variational expectation maximisation for time-series models. In Barber, D., Cemgil, A.T., Chiappa, S. (Eds.), Inference and Learning in Dynamic Models. Cambridge University Press.
Saxe, A., Bhand, M., Mudur, R., Suresh, B., Ng, A.Y. (2011). Unsupervised learning models of primary cortical receptive fields and receptive field plasticity.

2010

Hehrmann, P., Maier, J.K., Harper, N.S., McAlpine, D., Sahani, M. (2010). Adaptive Coding for Auditory Spatial Cues.
Orbanz, P., Teh, Y.W. (2010). Bayesian Nonparametric Models. In Sammut, C., Webb, G.I. (Eds.), Encyclopedia of Machine Learning. (pp. 81-89). New York, US: Springer.
Orbanz, P., Teh, Y.W. (2010). Bayesian Nonparametric Models. In Sammut, C., Webb, G.I. (Eds.), Encyclopedia of Machine Learning. (pp. 81-89). New York, US: Springer.
Blundell, C., Teh, Y.W., Heller, K.A. (2010). Bayesian Rose Trees.
Danafar, S., Gretton, A., Schmidhuber, J. (2010). Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition.
Orbanz, P. (2010). Conjugate Projective Limits.
Gretton, A., Gyorfi, L. (2010). Consistent Nonparametric Tests of Independence. Journal of Machine Learning Research, 11 1391-1423.
Williamson, S., Orbanz, P., Ghahramani, Z. (2010). Dependent Indian buffet processes.
Teh, Y.W. (2010). Dirichlet Processes. In Sammut, C., Webb, G.I. (Eds.), Encyclopedia of Machine Learning. (pp. 280-287). New York, US: Springer.
Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.P., Smola, A., ...Borgwardt, K.M. (2010). Discriminative frequent subgraph mining with optimality guarantees. Statistical Analysis and Data Mining, 3 (5), 302-318. doi:10.1002/sam.10084
Gretton, A., Fukumizu, K., Sriperumbudur, B.K. (2010). Discussion of: Brownian distance covariance. Annals of Applied Statistics, 3 (4), 1285-1294.
Fleming, S.M., Whiteley, L., Hulme, O.J., Sahani, M., Dolan, R.J. (2010). Effects of category-specific costs on neural systems for perceptual decision-making.. JOURNAL OF NEUROPHYSIOLOGY, 103 (6), 3238-3247. doi:10.1152/jn.01084.2009
Lin, I.-.C., Tuckerman, M.E. (2010). Enhanced Conformational Sampling of Peptides via Reduced Side-Chain and Solvent Masses. JOURNAL OF PHYSICAL CHEMISTRY B, 114 (48), 15935-15940. doi:10.1021/jp109865y
Rapela, J., Felsen, G., Touryan, J., Mendel, J.M., Grzywacz, N.M. (2010). ePPR: a new strategy for the characterization of sensory cells from input/output data. NETWORK-COMPUTATION IN NEURAL SYSTEMS, 21 (1-2), 35-90. doi:10.3109/0954898X.2010.488714
Teh, Y.W., Jordan, M.I. (2010). Hierarchical Bayesian Nonparametric Models with Applications. In Hjort, N., Holmes, C., Müller, P., Walker, S. (Eds.), Bayesian Nonparametrics: Principles and Practice. Cambridge University Press.
Sriperumbudur, B., Gretton, A., Fukumizu, K., Schoelkopf, B., Lanckriet, G. (2010). Hilbert Space Embeddings and Metrics on Probability Measures. Journal of Machine Learning Research, 11 1517-1561.
Gasthaus, J., Teh, Y.W. (2010). Improvements to the Sequence Memoizer.
Fukumizu, K., Song, L., Gretton, A. (2010). Kernel Bayes’ Rule.
Gasthaus, J., Wood, F., Teh, Y.W. (2010). Lossless compression based on the Sequence Memoizer.
Englitz, B., Ahrens, M., Tolnai, S., Rübsamen, R., Sahani, M., Jost, J. (2010). Multilinear models of single cell responses in the medial nucleus of the trapezoid body.. NETWORK-COMPUTATION IN NEURAL SYSTEMS, 21 (1-2), 91-124. doi:10.3109/09548981003801996
Yu, B.M., Santhanam, G., Sahani, M., Shenoy, K.V. (2010). Neural decoding for motor and communication prostheses. In Oweiss, K.G. (Ed.), Statistical Signal Processing for Neuroscience. (p. 219-263). Elsevier.
Sriperumbudur, B., Fukumizu, K., Gretton, A., Scholkopf, B., Lanckriet, G. (2010). Non-parametric estimation of integral probability metrics.
Song, L., Gretton, A., Guestrin, C. (2010). Nonparametric Tree Graphical Models.
Song, L., Gretton, A., Guestrin, C. (2010). Nonparametric tree graphical models via kernel embeddings.
Saxe, A.M., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y. (2010). On Random Weights and Unsupervised Feature Learning.
Bahrami, B., Olsen, K., Latham, P.E., Roepstorff, A., Rees, G., Frith, C.D. (2010). Optimally interacting minds.. SCIENCE, 329 (5995), 1081-1085. doi:10.1126/science.1185718
London, M., Roth, A., Beeren, L., Häusser, M., Latham, P.E. (2010). Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex.. NATURE, 466 (7302), 123-U142. doi:10.1038/nature09086
Turner, R.E., Sahani, M. (2010). STATISTICAL INFERENCE FOR SINGLE- AND MULTI-BAND PROBABILISTIC AMPLITUDE DEMODULATION.
Churchland, M.M., Yu, B.M., Cunningham, J.P., Sugrue, L.P., Cohen, M.R., Corrado, G.S., ...Scott, B.B. (2010). Stimulus onset quenches neural variability: a widespread cortical phenomenon.. NATURE NEUROSCIENCE, 13 (3), 369-U25. doi:10.1038/nn.2501

2009

Gretton, A., Fukumizu, K., Harchaoui, Z., Sriperumbudur, B. (2009). A Fast, Consistent Kernel Two-Sample Test.
Wood, F., Teh, Y.W. (2009). A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation.
Quon, G., Teh, Y.W., Chan, E., Brudno, M., Hughes, T., Morris, Q.D. (2009). A Mixture Model for the Evolution of Gene Expression in Non-homogeneous Datasets.
Wood, F., Archambeau, C., Gasthaus, J., James, L.F., Teh, Y.W. (2009). A Stochastic Memoizer for Sequence Data.
Berkes, P., Turner, R.E., Sahani, M. (2009). A structured model of video reproduces primary visual cortical organisation.. PLoS Comput Biol, 5 (9), e1000495-. doi:10.1371/journal.pcbi.1000495
Aeberhard, P.C., Arey, J.S., Lin, I.-.C., Rothlisberger, U. (2009). Accurate DFT Descriptions for Weak Interactions of Molecules Containing Sulfur. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 5 (1), 23-28. doi:10.1021/ct800299y
Görür, D., Teh, Y.W. (2009). An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering.
Linden, J.F., Orduna, I., Ahrens, M.B., Williamson, R.S., Mercado, E., Merzenich, M.M., Sahani, M. (2009). Auditory learning involving complex sounds affects nonlinear integration within cortical responses.
Fukumizu, K., Sriperumbudur, B., Gretton, A., Schoelkopf, B. (2009). Characteristic Kernels on Groups and Semigroups.
Gretton, A., Gyorfi, L. (2009). Consistent Nonparametric Tests of Independence. .
Orbanz, P. (2009). Construction of nonparametric Bayesian models from parametric Bayes equations.
Sahani, M., Ahrens, M.B., Linden, J.F. (2009). Contextual effects in neuronal responses to complex acoustic stimuli differ between areas AI and AAF.
Gasthaus, J., Wood, F., Görür, D., Teh, Y.W. (2009). Dependent Dirichlet Process Spike Sorting.
Salmasi, M., Modarres-Hashemi, M. (2009). Design and analysis of fractal detector for high resolution radars. CHAOS SOLITONS & FRACTALS, 40 (5), 2133-2145. doi:10.1016/j.chaos.2007.10.008
Peters, J., Janzing, D., Gretton, A., Schoelkopf, B. (2009). Detecting the Direction of Causal Time Series.
Peters, J., Janzing, D., Gretton, A., Schölkopf, B. (2009). Detecting the direction of causal time series.
Gretton, A., Fukumizu, K., Sriperumbudur, B. (2009). Discussion of: Brownian distance covariance. The Annals of Applied Statistics, 3 1285-1294.
Gretton, A., Fukumizu, K., Sriperumbudur, B. (2009). Discussion of: Brownian Distance Covariance. The Annals of Applied Statistics, 3 1285-1294.
Cascella, M., Lin, I.-.C., Tavernelli, I., Rothlisberger, U. (2009). Dispersion Corrected Atom-Centered Potentials for Phosphorus. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 5 (11), 2930-2934. doi:10.1021/ct9003756
Santhanam, G., Yu, B.M., Gilja, V., Ryu, S.I., Afshar, A., Sahani, M., Shenoy, K.V. (2009). Factor-analysis methods for higher-performance neural prostheses. Journal of Neurophysiology, doi:10.1152/jn.00097.2009
Shen, H., Jegelka, S., Gretton, A. (2009). Fast Kernel-Based Independent Component Analysis. IEEE Transactions on Signal Processing, 57 3498-3511.
Ganguli, S., Latham, P. (2009). Feedforward to the past: the relation between neuronal connectivity, amplification, and short-term memory. Neuron, 16 499-501.
Yu, B.M., Cunningham, J.P., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2009). Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Journal of Neurophysiology, doi:10.1152/jn.90941.2008
Yu, B.M., Cunningham, J.P., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2009). Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.
Jegelka, S., Gretton, A., Schoelkopf, B., Sriperumbudur, B., Luxburg, U.V. (2009). Generalized clustering via kernel embeddings.
Haffari, G.R., Teh, Y.W. (2009). Hierarchical Dirichlet Trees for Information Retrieval.
Arey, J.S., Aeberhard, P.C., Lin, I.-.C., Rothlisberger, U. (2009). Hydrogen Bonding Described Using Dispersion-Corrected Density Functional Theory. JOURNAL OF PHYSICAL CHEMISTRY B, 113 (14), 4726-4732. doi:10.1021/jp810323m
Lin, I.-.C., Seitsonen, A.P., Coutinho-Neto, M.D., Tavernelli, I., Rothlisberger, U. (2009). Importance of van der Waals Interactions in Liquid Water. JOURNAL OF PHYSICAL CHEMISTRY B, 113 (4), 1127-1131. doi:10.1021/jp806376e
Teh, Y.W., Görür, D. (2009). Indian Buffet Processes with Power-law Behavior.
Heller, K.A., Teh, Y.W., Görür, D. (2009). Infinite Hierarchical Hidden Markov Models.
Sriperumbudur, B., Fukumizu, K., Gretton, A., Lanckriet, G., Schoelkopf, B. (2009). Kernel choice and classifiability for RKHS embeddings of probability distributions.
Zhang, X., Song, L., Gretton, A., Smola, A. (2009). Kernel Measures of Independence for non-iid Data.
Baldassano, C.A., Franken, G.H., Mayer, J.R., Saxe, A.M., Yu, D.D. (2009). Kratos: Princeton University’s entry in the 2008 Intelligent Ground Vehicle Competition.
Blaschko, M., Gretton, A. (2009). Learning Taxonomies by Dependence Maximization.
Goodfellow, I., Lee, H., Le, Q., Saxe, A., Ng, A. (2009). Measuring Invariances in Deep Networks.
Tavernelli, I., Lin, I.-.C., Rothlisberger, U. (2009). Multicenter-type corrections to standard DFT exchange and correlation functionals. PHYSICAL REVIEW B, 79 (4), doi:10.1103/PhysRevB.79.045106
Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.-.P., Smola, A., ...Borgwardt, K. (2009). Near-optimal supervised feature selection among frequent subgraphs.
Tillman, R., Gretton, A., Spirtes, P. (2009). Nonlinear directed acyclic structure learning with weakly additive noise models.
Lücke, J., Turner, R.E., Sahani, M., Henniges, M. (2009). Occlusive Components Analysis.
Sriperumbudur, B., Fukumizu, K., A, G., Schoelkopf, B., Lanckriet, G. (2009). On integral probability metrics, φ-divergences and binary classification. .
Asuncion, A., Welling, M., Smyth, P., Teh, Y.W. (2009). On Smoothing and Inference for Topic Models.
Jacobs, A.L., Fridman, G., Douglas, R.M., Alam, N.M., Latham, P.E., Prusky, G.T., Nirenberg, S. (2009). Ruling out and ruling in neural codes. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 106 (14), 5936-5941. doi:10.1073/pnas.0900573106
Rao, V., Teh, Y.W. (2009). Spatial Normalized Gamma Processes.
Biessmann, F., Meinecke, F., Gretton, A., Rauch, A., Rainer, G., Logothetis, N., Mueller, K.-.R. (2009). Temporal Kernel CCA and its Application in Multimodal Neuronal Data Analysis. Machine Learning,
Gael, J.V., Teh, Y.W., Ghahramani, Z. (2009). The Infinite Factorial Hidden Markov Model.
Roy, D.M., Teh, Y.W. (2009). The Mondrian Process.
Doshi, F., Miller, K.T., Gael, J.V., Teh, Y.W. (2009). Variational Inference for the Indian Buffet Process.

2008

Cunningham, J.P., Shenoy, K.V., Sahani, M. (2008). .
Santhanam, G., Yu, B.M., Gilja, V., Ryu, S.I., Afshar, A., Sahani, M., Shenoy, K.V. (2008). A Factor-Analysis Decoder for High-Performance Neural Prostheses.
Gretton, A., Borgwardt, K., Rasch, M., Schoelkopf, B., Smola, A. (2008). A Kernel Method for the Two Sample Problem. .
Gretton, A., Fukumizu, K., Teo, C.H., Song, L., Schoelkopf, B., Smola, A. (2008). A Kernel Statistical Test of Independence. In Platt, J.C., Koller, D., Singer, Y., Roweis, S. (Eds.), Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.
Gretton, A., Fukumizu, K., Teo, C.H., Song, L., Schölkopf, B., Smola, A.J. (2008). A kernel statistical test of independence.
Ahrens, M.B., Sahani, M. (2008). A new framework for the perception of elapsed time replicates stimulus-driven expansion and contraction of apparent duration.
Lin, I.-.C., Roethlisberger, U. (2008). Atom-centered potentials to describe dispersion forces in density functional theory.
Whiteley, L.E., Sahani, M. (2008). Attention resolves the effects of a computational bottleneck: modelling binding, precueing, and task-driven bias.
Teh, Y.W., Daume, H., Roy, D.M. (2008). Bayesian Agglomerative Clustering with Coalescents.
Gael, J.V., Saatci, Y., Teh, Y.W., Ghahramani, Z. (2008). Beam Sampling for the Infinite Hidden Markov Model.
Salmasi, M., Golestani, S.J. (2008). Capacity of Broadcast Erasure Networks Using Spatial Network Coding.
Teh, Y.W., Kurihara, K., Welling, M. (2008). Collapsed Variational Inference for HDP.
Song, L., Smola, A., Borgwardt, K., Gretton, A. (2008). Colored Maximum Variance Unfolding. In Platt, J.C., Koller, D., Singer, Y., Roweis, S. (Eds.), Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.
Song, L., Smola, A., Borgwardt, K., Gretton, A. (2008). Colored maximum variance unfolding.
Borgwardt, K., Yan, X., Thoma, M., Cheng, H., Gretton, A., Song, L., ...Kriegel, H.-.P. (2008). Combining near-optimal feature selection with gSpan.
Ku, S.-.P., Gretton, A., Macke, J., Logothetis, N.K. (2008). Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. MAGNETIC RESONANCE IMAGING, 26 (7), 1007-1014. doi:10.1016/j.mri.2008.02.016
Sahani, M., Ahrens, M.B., Linden, J.F. (2008). Contextual effects in neuronal responses to complex acoustic stimuli differ between areas A1 and AAF.
Chieu, H.L., Lee, W.S., Teh, Y.W. (2008). Cooled and Relaxed Survey Propagation for MRFs.
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schoelkopf, B. (2008). Dataset Shift in Machine Learning.
Lin, I.-.C., Rothlisberger, U. (2008). Describing weak interactions of biomolecules with dispersion-corrected density functional theory. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 10 (19), 2730-2734. doi:10.1039/b718594d
Afshar, A., Yu, B.M., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2008). Evidence for smooth and inertial dynamics in the evolution of neural state.
Cunningham, J.P., Shenoy, K.V., Sahani, M. (2008). Fast Gaussian process methods for point process intensity estimation.
Turner, R.E., Berkes, P., Sahani, M. (2008). Finding the optimal sparse, overcomplete model for natural images by model selection.
Yu, B.M., Cunningham, J.P., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2008). Gaussian process factor analysis for low-dimensional single-trial analysis of neural population activity.
Welling, M., Teh, Y.W., Kappen, H.J. (2008). Hybrid Variational/Gibbs Collapsed Inference in Topic Models.
Whiteley, L., Sahani, M. (2008). Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes. Journal of Vision, 8 (3), 2, 1-15. doi:10.1167/8.3.2
Ahrens, M.B., Sahani, M. (2008). Inferring elapsed time from stochastic neural processes.
Ahrens, M.B., Sahani, M. (2008). Inferring Elapsed Time from Stochastic Neural Processes.
Ahrens, M.B., Paninski, L., Sahani, M. (2008). Inferring input nonlinearities in neural encoding models. Network: Computation in Neural Systems, 19 (1), 35-67.
Cunningham, J.P., Yu, B.M., Shenoy, K.V., Sahani, M. (2008). Inferring neural firing rates from spike trains using Gaussian Processes.
Cunningham, J.P., Yu, B.M., Shenoy, K.V., Sahani, M. (2008). Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes.
Rasch, M., Gretton, A., Murayama, Y., Maass, W., Logothetis, N. (2008). Inferring Spike Trains From Local Field Potentials. Journal of Neurophysiology, 99 1461-1476.
Sriperumbudur, B., Gretton, A., Fukumizu, K., Lanckriet, G., Schoelkopf, B. (2008). Injective Hilbert Space Embeddings of Probability Measures.
Fukumizu, K., Gretton, A., Sun, X., Schoelkopf, B. (2008). Kernel Measures of Conditional Dependence.
Fukumizu, K., Gretton, A., Sun, X., Schölkopf, B. (2008). Kernel measures of conditional dependence.
Peters, J., Janzing, D., Gretton, A., Schoelkopf, B. (2008). Kernel Methods for Detecting the Direction of Time Series.
Hehrmann, P., Dean, I., Ahrens, M.B., Harper, N.S., McAlpine, D., Sahani, M. (2008). Level-distribution-related changes in IC responses: adaptation or instantaneous non-linearity?.
Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M., Logothetis, N., Panzeri, S. (2008). Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information. Journal of Neuroscience, 28 5696-5709.
Lücke, J., Sahani, M. (2008). Maximal Causes for Non-Linear Component Extraction. Journal of Machine Learning Research, 9 1227-1267.
Turner, R.E., Sahani, M. (2008). Modeling natural sounds with modulation cascade processes.
Turner, R.E., Sahani, M. (2008). Modeling Natural Sounds with Modulation Cascade Processes.
Moh, Y., Orbanz, P., Buhmann, J.M. (2008). Music preference learning with partial information.
Fleming, S., Whiteley, L.E., Hulme, O.J., Sahani, M., Dolan, R. (2008). Networks for integrating sensory evidence and value in the human brain.
Pillow, J.W., Latham, P. (2008). Neural characterization in partially observed populations of spiking neurons.
Yu, B.M., Cunningham, J.P., Shenoy, K.V., Sahani, M. (2008). Neural Decoding of Movements: From Linear to Nonlinear Trajectory Model.
Ahrens, M.B., Linden, J.F., Sahani, M. (2008). Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods. Journal of Neuroscience, 28 (8), 1929-1942. doi:10.1523/jneurosci.3377-07.2008
Orbanz, P., Buhmann, J.M. (2008). Nonparametric Bayesian image segmentation. INTERNATIONAL JOURNAL OF COMPUTER VISION, 77 (1-3), 25-45. doi:10.1007/s11263-007-0061-0
Gretton, A., Gyorfi, L. (2008). Nonparametric Independence Tests: Space Partitioning and Kernel Approaches.
Berkes, P., Turner, R.E., Sahani, M. (2008). On Sparsity and Overcompleteness in Image Models.
Berkes, P., Turner, R., Sahani, M. (2008). On sparsity and overcompleteness in image models.
Roudi, R., Nirenberg, S., Latham, P. (2008). Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work.
Latham, P., Lengyel, M. (2008). Phase coding: spikes get a boost from local fields. Current Biology, 18 R349-R351.
Turner, R.E., Sahani, M. (2008). Probabilistic auditory scene analysis from natural statistics.
Beck, J.M., Ma, W.J., Kiani, R., Hanks, T., Churchland, A.K., Roitman, J., ...Pouget, A. (2008). Probabilistic population codes for Bayesian decision making. Neuron, 60 1142-1152.
Blaschko, M., Lampert, C., Gretton, A. (2008). Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis.
Hehrmann, P., Dean, I., Ahrens, M.B., Harper, N.S., McAlpine, D., Sahani, M. (2008). Separating the Impact of Non-Linearity and Parameter Change on Stimulus-Dependent Adaptation.
Song, L., Zhang, X., Smola, A., Gretton, A., Schoelkopf, B. (2008). Tailoring Density Estimation via Reproducing Kernel Moment Matching.
Blaschko, M., Gretton, A. (2008). Taxonomy Inference Using Kernel Dependence Measures. .
Christianson, G.B., Sahani, M., Linden, J.F. (2008). The consequences of response nonlinearities for interpretation of spectrotemporal receptive fields. Journal of Neuroscience, 28 (2), 446-455. doi:10.1523/JNEUROSCI.1775-07.2007
Ahrens, M.B., Sahani, M. (2008). Timing based on stochastic neural and sensory processes.
Cunningham, J.P., Yu, B.M., Gilja, V., Ryu, S.I., Shenoy, K.V., Sahani, M. (2008). Toward Optimal Estimates of Neural Firing Rates from Spike Trains.

2007

Roudi, Y., Latham, P.E. (2007). A balanced memory network. PLOS COMPUT BIOL, 3 (9), doi:10.1371/journal.pcbi.0030141.eor
Teh, Y.W., Newman, D., Welling, M. (2007). A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Song, L., Smola, A., Gretton, A., Borgwardt, K. (2007). A dependence maximization view of clustering.
Smola, A., Gretton, A., Song, L., Schoelkopf, B. (2007). A Hilbert Space embedding for distributions.
Smola, A., Gretton, A., Song, L., Schoelkopf, B. (2007). A Hilbert Space Embedding for Distributions.
Gretton, A., Borgwardt, K., Rasch, M., Schoelkopf, B., Smola, A. (2007). A Kernel Approach to Comparing Distributions. In Holte, R., Howe, A. (Eds.), Proceedings of the 22nd AAAI Conference on Artificial Intelligence. (p. 1637-1641). AAAI Press.
Gretton, A., Borgwardt, K., Rasch, M., Schoelkopf, B., Smola, A. (2007). A Kernel Method for the Two-Sample-Problem. In Schoelkopf, B., Platt, J., Hoffman, T. (Eds.), Advances in Neural Information Processing Systems 19. (pp. 513-520). Cambridge, MA: MIT Press.
Turner, R., Sahani, M. (2007). A maximum-likelihood interpretation for slow feature analysis. Neural Computation, 19 (4), 1022-1038. doi:10.1162/neco.2007.19.4.1022
Whiteley, L.E., Sahani, M. (2007). A unifying probabilistic computational framework for attention.
Robinson, B., Ahrens, M.B., Dean, I., McAlpine, D., Sahani, M. (2007). Adaptation to the Statistics of Sound Frequency in Mammalian Inferior Colliculus.
Ahrens, M.B., Sahani, M. (2007). Auditory alerts caused by changes in unattended sounds.
Orbanz, P., Braendle, S., Buhmann, J.M. (2007). Bayesian order-adaptive clustering for video segmentation.
Jegelka, S., Gretton, A. (2007). Brisk Kernel ICA.
Salmasi, M., Golestani, S.J. (2007). Capacity of erasure networks in the presence of spatial network coding.
Busse, L.M., Orbanz, P., Buhmann, J.M. (2007). Cluster analysis of heterogeneous rank data.
Kurihara, K., Welling, M., Teh, Y.W. (2007). Collapsed Variational Dirichlet Process Mixture Models.
Grzywacz, N.M., Cao, X., Rapela, J., Merwine, D. (2007). Comparison of responses of retinal ganglion cells to natural and artificial images.
Doye, J.P.K., Louis, A.A., Lin, I.-.C., Allen, L.R., Noya, E.G., Wilber, A.W., ...Lyus, R. (2007). Controlling crystallization and its absence: proteins, colloids and patchy models. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 9 (18), 2197-2205. doi:10.1039/b614955c
Huang, J., Smola, A., Gretton, A., Borgwardt, K., Schoelkopf, B. (2007). Correcting Sample Selection Bias by Unlabeled Data. In Schoelkopf, B., Platt, J., Hoffman, T. (Eds.), Advances in Neural Information Processing Systems 19. (pp. 601-608). Cambridge, MA: MIT Press.
Shen, H., Jegelka, S., Gretton, A. (2007). Fast Kernel ICA using an Approximate Newton Method.
Song, L., Bedo, J., Borgwardt, K., Gretton, A., Smola, A. (2007). Gene Selection via the BAHSIC Family of Algorithms. Bioinformatics (ISMB), 23 i490-i498.
Lücke, J., Sahani, M. (2007). Generalized Softmax Networks for Non-linear Component Extraction.
Cai, J.F., Lee, W.S., Teh, Y.W. (2007). Improving Word Sense Disambiguation Using Topic Features.
Prince, S., Aghajanian, J., Mohammed, U., Sahani, M. (2007). Latent Identity Variables: Biometric Matching without Explicit Identity Estimation.
Lücke, J., Sahani, M. (2007). Learning in a Generative Model with Competitive Combination Is Approximated by (Soft-)WTA Networks.
Lin, I.-.C., Coutinho-Neto, M.D., Felsenheimer, C., von Lilienfeld, O.A., Tavernelli, I., Rothlisberger, U. (2007). Library of dispersion-corrected atom-centered potentials for generalized gradient approximation functionals: Elements H, C, N, O, He, Ne, Ar, and Kr. PHYSICAL REVIEW B, 75 (20), doi:10.1103/PhysRevB.75.205131
Linden, J.F., Christianson, G.B., Sahani, M. (2007). Linear modelling of non-linear response functions: stimulus-specific STRFs without adaptation.
Yu, B.M., Kemere, C., Santhanam, G., Afshar, A., Ryu, S.I., Meng, T.H., ...Shenoy, K.V. (2007). Mixture of Trajectory Models for Neural Decoding of Goal-Directed Movements.. Journal of Neurophysiology, 97 (5), 3763-3780. doi:10.1152/jn.00482.2006
Turner, R.E., Sahani, M. (2007). Modeling sounds with Gaussian modulation cascade processes.
Ahrens, M.B., Paninski, L., Petersen, R., Linden, J.F., Sahani, M. (2007). Multilinear neural encoding models capture nonlinearities and contextual influences in cortical responses.
Yu, B.M., Cunningham, J.P., Shenoy, K.V., Sahani, M. (2007). Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models.
Cordes, S., Gallistel, C.R., Gelman, R., Latham, P.E. (2007). Nonverbal arithmetic in humans: Light from noise.. Perception and Psychophysics,
Cai, J.F., Lee, W.S., Teh, Y.W. (2007). NUS-ML: Improving Word Sense Disambiguation Using Topic Features.
Gilja, V., Santhanam, G., Linderman, M.D., Afshar, A., Ryu, S.I., Meng, T.H., ...Shenoy, K.V. (2007). Optimizing spike sorting for brain computer interfaces with non-stationary waveforms.
Lin, I.-.C., von Lilienfeld, O.A., Coutinho-Neto, M.D., Tavernelli, I., Rothlisberger, U. (2007). Predicting noncovalent interactions between aromatic biomolecules with London-dispersion-corrected DFT. JOURNAL OF PHYSICAL CHEMISTRY B, 111 (51), 14346-14354. doi:10.1021/jp0750102
Turner, R.E., Sahani, M. (2007). Probabilistic Amplitude Demodulation.
Beck, J.M., Ma, W.J., Latham, P.E., Pouget, A. (2007). Probabilistic population codes and the exponential family of distributions. Progress in Brain Research, 165 509-519.
Atreya, A.R., Cattle, B.C., Collins, B.M., Essenburg, B., Franken, G.H., Saxe, A.M., ...Kornhauser, A.L. (2007). Prospect eleven: Princeton University's entry in the 2005 DARPA Grand Challenge. In (pp. 349-361). .
Berkes, P., Turner, R.E., Sahani, M. (2007). Simple and complex cells as style and content variables in a bilinear model based on temporal stability.
Churchland, M.M., Yu, B.M., Sahani, M., Shenoy, K.V. (2007). Single-trial activity patterns and the need for large-scale neural recordings. Current Opinion in Neurobiology, 17 (5), 609-618. doi:10.1016/j.conb.2007.11.001
Fukumizu, K., Bach, F., Gretton, A. (2007). Statistical Consistency of Kernel Canonical Correlation Analysis. J. Mach. Learn. Res., 8 361-383.
Teh, Y.W., Görür, D., Ghahramani, Z. (2007). Stick-breaking Construction for the Indian Buffet Process.
Song, L., Smola, A., Gretton, A., Borgwardt, K., Bedo, J. (2007). Supervised Feature Selection via Dependence Estimation.
Lim, Y.J., Teh, Y.W. (2007). Variational Bayesian Approach to Movie Rating Prediction.
Tapavicza, E., Lin, I.-.C., von Lilienfeld, O.A., Tavernelli, I., Coutinho-Neto, M.D., Rothlisberger, U. (2007). Weakly bonded complexes of aliphatic and aromatic carbon compounds described with dispersion corrected density functional theory. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 3 (5), 1673-1679. doi:10.1021/ct700049s

2006

Teh, Y.W. (2006). A Bayesian Interpretation of Interpolated Kneser-Ney. School of Computing, National University of Singapore .
Hinton, G.E., Osindero, S., Teh, Y.W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18 (7), 1527-1554. doi:10.1162/neco.2006.18.7.1527
Teh, Y.W. (2006). A Hierarchical Bayesian Language Model based on Pitman-Yor Processes.
Davy, M., Desobry, F., Gretton, A., Doncarli, C. (2006). An Online Support Vector Machine for Abnormal Events Detection. Signal Processing, 86 2009-2025.
Ma, W.J., Beck, J.M., Latham, P.E., Pouget, A. (2006). Bayesian inference with probabilistic population codes.. Nature Neuroscience, 9 (11), 1432-1438. doi:10.1038/nn1790
Xing, E.P., Sohn, K., Jordan, M.I., Teh, Y.W. (2006). Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture.
Latham, P., Pouget, A. (2006). Computing with population codes. In Doya, K., Ishii, S., Pouget, A., Rao, R. (Eds.), Bayesian Brain. Csmbridge: MIT Press.
Rapela, J., Mendel, J.M., Grzywacz, N.M. (2006). Estimating nonlinear receptive fields from natural images. JOURNAL OF VISION, 6 (4), 441-474. doi:10.1167/6.4.11
Yu, B.M., Shenoy, K.V., Sahani, M. (2006). Expectation propagation for inference in non-linear dynamical models with Poisson observations.
Yu, B.M., Afshar, A., Santhanam, G., Ryu, S., Shenoy, K.V., Sahani, M. (2006). Extracting dynamical structure embedded in neural activity..
Shen, H., Jegelka, S., Gretton, A. (2006). Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function. .
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M. (2006). Hierarchical Dirichlet Processes. Journal of the American Statistical Association, 101 (476), 1566-1581. doi:10.1198/016214506000000302
Borgwardt, K., Gretton, A., Rasch, M., Kriegel, H.-.P., Schoelkopf, B., Smola, A. (2006). Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics (ISMB), 22 e49-e57.
Ahrens, M.B., Linden, J.F., Sahani, M. (2006). Multilinear spectrotemporal models for predicting auditory cortical responses.
Averbeck, B.B., Latham, P.E., Pouget, A.P. (2006). Neural correlations, population coding and computation. Nature Reviews Neuroscience, 7 (5), 358-366. doi:10.1038/nrn1888
Whiteley, L.E., Sahani, M. (2006). Observers' decisions in a simple visual task are consistent with Bayesian processing of early perceptual uncertainty.
Atreya, A.R., Cattle, B.C., Collins, B.M., Essenburg, B., Franken, G.H., Saxe, A.M., ...Kornhauser, A.L. (2006). Prospect Eleven: Princeton University’s entry in the 2005 DARPA Grand Challenge. Journal of Field Robotics, 23 745-753. doi:10.1002/rob.20141
Lee, W.S., Zhang, X., Teh, Y.W. (2006). Semi-supervised Learning in Reproducing Kernel Hilbert Spaces Using Local Invariances. .
Orbanz, P., Buhmann, J.M. (2006). Smooth image segmentation by nonparametric Bayesian inference.
Fukumizu, K., Bach, F., Gretton, A. (2006). Statistical Convergence of Kernel CCA.
Gretton, A., Belitski, A., Murayama, Y., Schoelkopf, B., Logothetis, N. (2006). The effect of artifacts on dependence measurement in fMRI. Magnetic Resonance Imaging, 24 401-409.
Hinton, G.E., Osindero, S., Welling, M., Teh, Y.W. (2006). Unsupervised Discovery of Non-Linear Structure using Contrastive Backpropagation. Cognitive Science, 30 (4), 725-731. doi:10.1207/s15516709cog0000_76

2005

Fukumizu, K., Bach, F., Gretton, A. (2005). Consistency of Kernel Canonical Correlation Analysis. .
Linden, J.F., Orduna, I., Mercado, E., Merzenich, M.M., Sahani, M. (2005). Degree of linearity in auditory cortical responses is less plastic than form of linear receptive fields.
Yu, B.M., Afshar, A., Santhanam, G., Ryu, S.I., Shenoy, K.V., Sahani, M. (2005). Extracting Dynamical Structure Embedded in Motor Preparatory Activity.
Sahani, M., Yu, B.M., Afshar, A., Santhanam, G., Ryu, S.I., Shenoy, K.V. (2005). Extracting dynamical structure embedded in neural activity.
Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., ...Logothetis, N. (2005). Kernel Constrained Covariance for Dependence Measurement.
Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schoelkopf, B. (2005). Kernel Methods for Measuring Independence. Journal of Machine Learning Research, 6 2075-2129.
Edwards, J., Teh, Y.W., Forsyth, D.A., Bock, R., Maire, M., Vesom, G. (2005). Making Latin Manuscripts Searchable using gHMM’s.
Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B. (2005). Measuring Statistical Dependence with Hilbert-Schmidt Norms. .
Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B. (2005). Measuring Statistical Dependence with Hilbert-Schmidt Norms.
Yu, B.M., Kemere, C., Santhanam, G., Afshar, A., Ryu, S.I., Meng, T.H., ...Shenoy, K.V. (2005). Mixture of trajectory models for neural decoding of goal-directed movements.
Orbanz, P., Buhmann, J.M. (2005). SAR images as mixtures of Gaussian mixtures.
Teh, Y.W., Seeger, M., Jordan, M.I. (2005). Semiparametric Latent Factor Models.
Seeger, M., Teh, Y.W., Jordan, M.I. (2005). Semiparametric Latent Factor Models. .
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M. (2005). Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
Welling, M., Minka, T., Teh, Y.W. (2005). Structured Region Graphs: Morphing EP into GBP.
Latham, P.E., Nirenberg, S. (2005). Synergy, Redundancy, and Independence in Population Codes, Revisited. Journal of Neuroscience, 25 5195-5206. doi:10.1523/JNEUROSCI.5319-04.2005
Latham, P.E., Dayan, P. (2005). Touche: the Feeling of Choice. Nature Neuroscience, 8 408-409.

2004

Sahani, M. (2004). A biologically plausible algorithm for reinforcement-shaped representational learning..
Santhanam, G., Sahani, M., Ryu, S., Shenoy, K. (2004). An extensible infrastructure for fully automated spike sorting during online experiments..
Welling, M., Rosen-Zvi, M., Teh, Y.W. (2004). Approximate Inference by Markov Chains on Union Spaces.
Linden, J.F., Orduna, I., Sahani, M., Mercado, E., Merzenich, M.M. (2004). Auditory learning involving complex sounds increases the spectrotemporal complexity of cortical receptive fields.
Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Schoelkopf, B., Logothetis, N. (2004). Behaviour and Convergence of the Constrained Covariance. .
Latham, P.E., Nirenberg, S. (2004). Computing and stability in cortical networks. Neural Computation, 16 1385-1412.
Welling, M., Teh, Y.W. (2004). Linear response algorithms for approximate inference in graphical models.. Neural Computation, 16 (1), 197-221. doi:10.1162/08997660460734056
Welling, M., Teh, Y.W. (2004). Linear response for approximate inference.
BakIr, G., Gretton, A., Franz, M., Schoelkopf, B. (2004). Multivariate Regression via Stiefel Manifold Constraints.
BakIr, G., Gretton, A., Franz, M., Schoelkopf, B. (2004). Multivariate Regression with Stiefel Constraints. .
Berg, T.L., Berg, A.C., Edwards, J., Maire, M., White, R., Teh, Y.W., ...Forsyth, D.A. (2004). Names and faces in the news.
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schoelkopf, B. (2004). Ranking on Data Manifolds.
Seriès, P., Latham, P.E., Pouget, A. (2004). Tuning Curve Sharpening for Orientation Selectivity: Coding Efficiency and the Impact of Correlations. Nature Neuroscience, 7 (10), 1129-1135. doi:10.1038/nn1321

2003

Dayan, P., Sahani, M., Deback, G. (2003). Adaptation and Unsupervised Learning.
Tabak, J., Latham, P. (2003). Analysis of spontaneous bursting activity in random neural networks. NeuroReport, 14 1445-1449.
Welling, M., Teh, Y.W. (2003). Approximate Inference in Boltzmann Machines. Artificial Intelligence, 143 (1), 19-50. doi:10.1016/S0004-3702(02)00361-2
Teh, Y.W., Roweis, S. (2003). Automatic Alignment of Local Representations.
Teh, Y.W. (2003). Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models.
Nirenberg, S., Latham, P.E. (2003). Decoding neuronal spike trains: how important are correlations?. Proceedings of the National Academy of Sciences of the United States of America, 100 7348-7353.
Pouget, A., Latham, P. (2003). Decoding population codes. In Arbib, M. (Ed.), Handbook of brain theory and neural networks. Csmbridge: MIT press.
Sahani, M., Dayan, P. (2003). Doubly distributional population codes: simultaneous representation of uncertainty and multiplicity.. Neural Computation, 15 (10), 2255-2279. doi:10.1162/089976603322362356
Teh, Y.W., Welling, M., Osindero, S., Hinton, G.E. (2003). Energy-Based Models for Sparse Overcomplete Representations. Journal of Machine Learning Research, 4 1235-1260.
Sahani, M., Linden, J.F. (2003). Evidence optimization techniques for estimating stimulus-response functions.. Advances in Neural Information Processing Systems, 15 301-308.
Sahani, M., Linden, J.F. (2003). Evidence optimization techniques for estimating stimulus-response functions.. In Becker, S., Thrun, S., Obermayer, K. (Eds.), Advances in Neural Information Processing Systems. (pp. 109-116). Cambridge, MA, USA: MIT Press.
Brunel, N., Latham, P.E. (2003). Firing rate of the noisy quadratic integrate-and-fire neuron. Neural Computation, 15 2281-2306.
Sahani, M., Linden, J.F. (2003). How linear are auditory cortical neurons?. Advances in Neural Information Processing Systems, 15 109-116.
Sahani, M., Linden, J.F. (2003). How linear are auditory cortical responses?. In Becker, S., Thrun, S., Obermayer, K. (Eds.), Advances in Neural Information Processing Systems. (pp. 301-308). Cambridge, MA, USA: MIT Press.
Santhanam, G., Churchland, M.M., Sahani, M., Shenoy, K.V. (2003). Local field potential activity varies with reach distance, direction, and speed in monkey pre-motor cortex..
Teh, Y.W., Welling, M. (2003). On Improving the Efficiency of the Iterative Proportional Fitting Procedure.
Gretton, A., Desobry, F. (2003). On-Line One-Class Support Vector Machines: An Application to Signal Segmentation..
Latham, P., Deneve, S., Pouget, A. (2003). Optimal Computations with Attractor Networks. Journal of Physiology (Paris), 97 (4-6), 683-694. doi:10.1016/j.jphysparis.2004.01.022
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schoelkopf, B. (2003). Ranking on Data Manifolds. .
Kemere, C., Sahani, M., Meng, T. (2003). Robust neural decoding of reaching movements for prosthetic systems.
Linden, J.F., Liu, R.C., Sahani, M., Schreiner, C.E., Merzenich, M.M. (2003). Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex. Journal of Neurophysiology, 90 (4), 2660-2675. doi:10.1152/jn.00751.2002
Gretton, A., Herbrich, R., Smola, A. (2003). The Kernel Mutual Information. .
Gretton, A., Herbrich, R., Smola, A. (2003). The Kernel Mutual Information.

2002

Gallistel, C.R., Mark, T.A., King, A.P., Latham, P.E. (2002). A test of Gibbon's feedforward model of matching. Learning and Motivation, 33 46-62.
Kakade, S., Teh, Y.W., Roweis, S. (2002). An Alternate Objective Function for Markovian Fields.
Latham, P. (2002). Associative memory in realistic neuronal networks.
Pouget, A., Latham, P. (2002). Digitized neural networks: long-term stability from forgetful neurons. Nature Neuroscience, 5 709-710.
Shattuck, D.W., Rapela, J., Asma, E., Chatzioannou, A., Qi, J., Leahy, R.M. (2002). Internet2-based 3D PET image reconstruction using a PC cluster.
Davy, M., Gretton, A., Doucet, A., Rayner, P. (2002). Optimized Support Vector Machines for Nonstationary Signal Classification. IEEE Signal Processing Letters, 9 442-445.
Linden, J.F., Liu, R.C., Sahani, M., Schreiner, C.E., Merzenich, M.M. (2002). Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex.
Orduna, I., Linden, J.F., Mercado, E., Sahani, M., Merzenich, M.M., Gluck, M.A.O., ...M, M. (2002). Spectrotemporally inseparable auditory receptive fields induced by discrimination training.
Pesaran, B., Pezaris, J.S., Sahani, M., Mitra, P.P., Andersen, R.A. (2002). Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nature Neuroscience, 5 (8), 805-811. doi:10.1038/nn890
Sahani, M., Linden, J.F. (2002). The components of spectrotemporal receptive fields in auditory cortex.
Teh, Y.W., Welling, M. (2002). The unified propagation and scaling algorithm.

2001

Hinton, G.E., Welling, M., Teh, Y.W., Osindero, S. (2001). A New View of ICA.
Rapela, J. (2001). Automatically Combining Ranking Heuristics for HTML Documents.
Welling, M., Teh, Y.W. (2001). Belief Optimization for Binary Networks: a Stable Alternative to Loopy Belief Propagation.
Hinton, G.E., Teh, Y.W. (2001). Discovering Multiple Constraints that are Frequently Approximately Satisfied.
Deneve, S., Latham, P., Pouget, A. (2001). Efficient Computation and Cue Integration with Noisy Population Codes. Nature Neuroscience, 4 (8), 826-831. doi:10.1038/90541
Gretton, A., Herbrich, R., Chapelle, O., Schoelkopf, B., Rayner, P. (2001). Estimating the Leave-One-Out Error for Classification Learning with SVMs. .
Gretton, A., Davy, M., Doucet, A., Rayner, P. (2001). Nonstationary Signal Classification using Support Vector Machines.
Teh, Y.W., Welling, M. (2001). Passing and Bouncing Messages for Generalised Inference. London, UK: Gatsby Computational Neuroscience Unit.
Teh, Y.W., Hinton, G.E. (2001). Rate-coded restricted Boltzmann machines for face recognition.
Nirenberg, S., Carcieri, S.M., Jacobs, A.L., Latham, P. (2001). Retinal ganglion cells act largely as independent encoders. Nature, 411 (6838), 698-701. doi:10.1038/35079612
Linden, J.F., Liu, R.C., Sahani, M., Kvale, M., Schreiner, C.E., Merzenich, M.M. (2001). Reverse-correlation analysis of receptive fields in mouse and rat auditory cortex.
Sahani, M., Linden, J.F. (2001). Robust regression and non-linear kernel methods for characterization of neuronal response functions from limited data.
Gretton, A., Doucet, A., Herbrich, R., Rayner, P., Schoelkopf, B. (2001). Support Vector Regression for Black-Box System Identification.
Gallistel, C.R., Mark, T.A., King, A.P., Latham, P.E. (2001). The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect. Journal of Experimental Psychology: Animal Behavior Processes, 27 354-372.
Pouget, A., Deneve, S., Latham, P. (2001). The relevance of Fisher Information for theories of cortical computation and attention. In Braun, J., Koch, C., Davis, J. (Eds.), Visual attention and cortical circuits. Csmbridge: MIT Press.

2000

Sahani, M., Linden, J.F. (2000). A probabilistic framework for interpreting plasticity in cortical maps and receptive fields.
Latham, P., Richmond, B., Nelson, P., Nirenberg, S. (2000). Intrinsic dynamics in neuronal networks. I. Theory. Journal of Neurophysiology, 83 808-827.
Latham, P., Richmond, B., Nirenberg, S., Nelson, P. (2000). Intrinsic dynamics in neuronal networks. II. Experiment. Journal of Neurophysiology, 83 828-835.
Hinton, G.E., Ghahramani, Z., Teh, Y.W. (2000). Learning to parse images.

1999

Gretton, A. (1999). Design of Chimes to Produce Consonant, Non-Harmonic Scales. Acoustics Australia, 27
Deneve, S., Pouget, A., Latham, P. (1999). Heeger's normalization, line attractor networks, and ideal observers.
Pouget, A., Deneve, S., Ducom, J., Latham, P. (1999). Narrow versus wide tuning curves: what's best for a population code?. Neural Computation, 11 85-90.
Deneve, S., Latham, P., Pouget, A. (1999). Reading population codes: a neural implementation of ideal observers. Nature Neuroscience, 2 740-745.

1998

Gershon, E., Weiner, M., Latham, P., Richmond, B. (1998). Coding strategies in monkey V1 and inferior temporal cortices. Journal of Neurophysiology, 79 1135-1144.
Sahani, M., Pezaris, J.S., Andersen, R.A. (1998). Extracellular recording from multiple neighboring cells: A maximum-likelihood solution to the spike-separation problem.
Bacchus, F., Teh, Y.W. (1998). Making Forward Chaining Relevant.
Nirenberg, S., Latham, P. (1998). Population coding in the retina. Current Opinion Neurobiology, 8 488-493.
Pouget, A., Zhang, K., Deneve, S., Latham, P. (1998). Statistically efficient estimation using population coding. Neural Computation, 10 373-401.

1997

Blank, M., Danly, B., Levush, B., Latham, P., Pershing, D. (1997). Experimental demonstration of a W-band gyroklystron amplifier. Physical Review Letters, 79 4485-4488.

1995

Lawson, W., Latham, P., Calame, J., Cheng, J., Hogan, B., Nusinovich, G., ...Reiser, M. (1995). High power operation of first and second harmonic gyrotwystrons. Journal of Applied Physics, 78 550-559.
Guo, H., Hoppe, D., Rodgers, J., Perez, R., Tate, J., onroy, B., ...Nusinovich, G. (1995). Phase-locking of a second-harmonic gyrotron oscillator using a quasi-optical circulator to separate injection and output signals. IEEE Transactions on Plasma Science, 23 822-832.
Latham, P., Nusinovich, G. (1995). Stability analysis of relativistic gyro-traveling wave devices. Physics of Plasmas, 2 3511-3523.
Nusinovich, G., Latham, P., Dumbrajs, O. (1995). Theory of relativistic cyclotron masers. Physical Review E, 52 998-1012.
Latham, P., Nusinovich, G. (1995). Theory of relativistic gyro-traveling wave devices. Physics of Plasmas, 2 3494-3510.

1994

Calame, J., Cheng, J., Latham, P., Lawson, W., Hogan, B., Matthews, H., ...Striffler, C. (1994). Amplification studies of a two-cavity second harmonic gyroklystron with a mixed-mode output cavity.. Journal of Applied Physics, 75 4721-4730.
Nusinovich, G., Latham, P., Li, H. (1994). Efficiency of frequency up-shifted gyrodevices: cyclotron harmonics versus CARM's.. IEEE Transactions on Plasma Science, 22 796-803.
Matthews, H., Lawson, W., Calame, J., Flaherty, M., Hogan, B., Cheng, J., Latham, P. (1994). Experimental studies of stability and amplification in a two-cavity second harmonic gyroklystron. IEEE Transactions on Plasma Science, (22), 825-833.
Latham, P., Lawson, W., Irwin, V., Hogan, B., Nusinovich, G., Matthews, H., Flaherty, M. (1994). High power operation of an X-band gyrotwistron. Physical Review Letters, 72 3730-3733.
Calame, J., Cheng, J., Hogan, B., Lawson, W., Striffler, C., Latham, P., Irwin, V. (1994). Measurements of velocity ratio in a 90 MW gyroklystron electron beam. IEEE Transactions on Plasma Science, 72 3730-3733.
Latham, P., Lawson, W., Irwin, V. (1994). The design of a 100 MW, Ku band second harmonic gyroklystron experiment. IEEE Transactions on Plasma Science, 22 804-817.
Latham, P., Levush, B.N., G, P., S. (1994). Theory of phase-locked gyrotrons operating at cyclotron harmonics. IEEE Transactions on Plasma Science, 22 818-824.

1993

Lawson, W., Matthews, H., Lee, M., Calame, J., Hogan, B., Cheng, J., ...Reiser, M. (1993). High-power operation of a K-band second harmonic gyroklystron. Physical Review Letters, 71 456-459.
Latham, P., Granatstein, V., Carmel, Y. (1993). Phase locking and bandwidth in a gyrotron oscillator. International Journal of Infrared Millimeter Waves, 14 1217-1227.

1992

Tantawi, S., Main, W., Latham, P., Nusinovich, G., Lawson, W., Striffler, C., Granatstein, V. (1992). High-power X-band amplification from an overmoded three-cavity gyroklyston wit h a tunable penultimate cavity. IEEE Transactions on Plasma Science, 20 205-215.
Lawson, W., Latham, P. (1992). The scattering matrix formulation for overmoded coaxial cavities. IEEE Transactions on Microwave Theory and Technique, 40 1973-1977.
Latham, P., Finn, J., Booske, J. (1992). Transverse mode interference in systems with discrete energy levels: applications to waveguide filters. International Journal of Electronics, 72 273-304.
Latham, P., Miller, S., Striffler, C. (1992). Use of Lie transforms to generalize Madey's theorem for computing the gain in microwave devices. Physical Review A, 45 1197-1206.

1991

Bidwell, S., Radack, D., Antonsen, T., Booske, J., Carmel, Y., Destler, W., ...Mayergoyz, I. (1991). A high-average-power tapered FEL amplifier at submillimeter frequencies using sheet electron beams and short-period wigglers. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, A304 187-191.
Lawson, W., Calame, J., Hogan, B., Latham, P., Read, M., Granatstein, V., ...Striffler, C. (1991). Efficient operation of a high-power X-band gyroklystron. Physical Review Letters, 67 520-523.
Calame, J., Lawson, W., Granatstein, V., Latham, P., Hogan, B., Striffler, C., ...Main, W. (1991). Experimental studies of stability and amplification in four overmoded, two-cavity gyroklystrons operating at 9.87 GHz. Journal of Applied Physics, 70 2423-2434.
Latham, P., Levush, B., Antonsen, T., Metzler, N. (1991). Harmonic operation of a free-electron laser. Physical Review Letters, 66 1442-1445.
Park, G., Granatstein, V., Latham, P., Armstrong, C., Ganguly, A., Park, S. (1991). Phase stability of gyroklystron amplifier. IEEE Transactions on Plasma Science, 19 632-640.

1990

Latham, P. (1990). AC space-charge effects in gyroklystron amplifiers. IEEE Transactions on Plasma Science, 18 273-285.
Booske, J., Radack, D., Antonsen, T., Bidwell, S., Carmel, Y., Destler, W., ...Levush, B. (1990). Design of high-average-power, near-millimeter free electron laser oscillators using short-period wigglers and sheet electron beam. IEEE Transactions on Plasma Science, 18 399-415.
Booske, J., Granatstein, V., Radack, D., Antonsen, T., Bidwell, S., Carmel, Y., ...Mayergoyz, I. (1990). High-average-power CW FELs for application to plasma heating: designs and experiments. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, A296 791-796.
Latham, P., Levush, B. (1990). The interaction of high- and low-frequency waves in a free-electron laser. IEEE Transactions on Plasma Science, 18 472-481.

1989

Neilson, J., Latham, P., Caplan, M., Lawson, W. (1989). Determination of the resonant frequencies in a complex cavity using the scattering matrix formulation. IEEE Transactions on Microwave Theory and Technique, 37 1165-1170.
Booske, J., Granatstein, V., Antonsen, T., Destler, W., Finn, J., Latham, P., ...Rodgers, J. (1989). Free-electron laser with small period wiggler and sheet electron beam: a study of the feasibility of operation at 300 GHz with 1 MW CW output power. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, A285 92-96.
Latham, P. (1989). Linear-analysis of a free-electron laser coupled to betatron oscillations. Physics of Fluids B, 1 2085-2098.
Latham, P. (1989). The use of a single source to drive a binary peak power multiplier. IEEE Transactions on Microwave Theory and Technique, 37 929-931.

1988

Latham, P. (1988). An FEL driven by transverse gradients in the wiggler field. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, A272 (442), 447-.
Antonsen, T., Latham, P. (1988). Linear theory of a sheet beam free electron laser. Physics of Fluids, 31 3379-3386.
Chu, K., Latham, P., Granatstein, V. (1988). Penultimate cavity tuning of the gyroklystron amplifier. International Journal of Electronics, 65 419-428.

1987

Bluem, H., Latham, P., Lawson, W., Striffler, C. (1987). Single-particle motion in a large-orbit gyrotron. IEEE Transactions on Microwave Theory and Technique, 35 946-955.
Lawson, W., Latham, P. (1987). The design of a small-orbit/large-orbit gyroklystron experiment. Journal of Applied Physics, 61 519-528.

1985

Chu, K., Granatstein, V., Latham, P., Lawson, W., Striffler, C. (1985). A 30-MW gyroklystron-amplifier design for high-energy linear accelerators. Chu,K.,Granatstein,V.,Latham,P.,Lawson,W.,Striffler,C.. IEEE Transactions on Plasma Science, 13 424-434.

1982

Abarbanel, H., Latham, P. (1982). Finite resolution approximation to the asymptotic distribution for dynamical systems. Physics Letters, 89A 55-58.

1900

Baum, J., Kanagawa, H., Gretton, A. (1900). A kernel Stein test of goodness of fit for sequential models. arXiv.
Xu, L., Gretton, A. (1900). A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. arXiv.
Wenliang, L.K., Sahani, M. (1900). A neurally plausible model for online recognition and postdiction in a dynamical environment. .
Park, M., Jitkrittum, W., Qamar, A., Szabo, Z., Buesing, L., Sahani, M. (1900). Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM). Presented at: Quinquennial Review Symposium Gatsby Unit, London, UK.
Pezzotta, A., Adorisio, M., Celani, A. (1900). Chemotaxis emerges as the optimal solution to cooperative search games. Physical Review E, 98 (4), doi:10.1103/physreve.98.042401
Görür, D., Teh, Y.W. (1900). Concave-Convex Adaptive Rejection Sampling. Journal of Computational and Graphical Statistics, doi:10.1198/jcgs.2011.09058
Najafi, F., Elsayed, G.F., Cao, R., Pnevmatikakis, E., Latham, P.E., Cunningham, J.P., Churchland, A.K. (1900). Dataset from "Farzaneh Najafi, Gamaleldin F Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P Cunningham, Anne K Churchland (bioRxiv, 2018); Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.”. Cold Spring Harbor Laboratory.
Amjad, J. (1900). Deep Learning for Inverse Problems: Performance Characterizations, Learning Algorithms, and Applications. (Doctoral Dissertation). UCL (University College London).
O’Shea, D.J., Duncker, L., Goo, W., Sun, X., Vyas, S., Trautmann, E.M., ...Sahani, M. (1900). Direct neural perturbations reveal a dynamical mechanism for robust computation.
Jitkrittum, W., Szabo, Z., Chwialkowski, K., Gretton, A. (1900). Distinguishing distributions with interpretable features. Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop New York, USA.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (1900). Distribution Regression - Make It Simple and Consistent. Presented at: Data, Learning and Inference workshop (DALI) La Palma (Canaries, Spain).
Chwialkowski, K., Ramdas, A., Sejdinovic, D., Gretton, A. (1900). Fast Two-Sample Testing with Analytic Representations of Probability Measures.
Pezzotta, A., Adorisio, M., Celani, A. (1900). From conformational spread to allosteric and cooperative models of E. coli flagellar motor. Journal of Statistical Mechanics: Theory and Experiment, 2017 (2), 023402. doi:10.1088/1742-5468/aa569e
Pedrosa, V., Menichini, E., Pajot-Moric, Q., Vincent, P., Zhou, L., Teachen, L., ...Akrami, A. (1900). Humans, rats and mice show species-specific adaptations to sensory statistics in categorisation behaviour.
Gretton, A. (1900). Kernel Methods for Classification and Signal Separation (PhD thesis). (Doctoral Dissertation).
Jitkrittum, W., Gretton, A., Heess, N., Eslami, A., Lakshminarayanan, B., Sejdinovic, D., Szabo, Z. (1900). Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. Presented at: Data, Learning and Inference workshop (DALI) La Palma (Canaries, Spain).
Jitkrittum, W., Gretton, A.L., Heess, N., Eslami, S.M.A., Lakshminarayanan, B., Sejdinovic, D., Szabó, Z. (1900). Kernel-Based Just-In-Time Learning For Passing Expectation Propagation Messages. Presented at: International Conference on Machine Learning (ICML) - Large-Scale Kernel Learning: Challenges and New Opportunities workshop Lille, France.
Chadwick, A., Khan, A., Poort, J., Blot, A., Hofer, S., Mrsic-Flogel, T., Sahani, M. (1900). Learning shapes cortical dynamics to enhance integration of relevant sensory input. .
Berg, T.L., Berg, A.C., Edwards, J., Maire, M., White, R., Teh, Y.W., ...Forsyth, D.A. (1900). Names and Faces. International Journal of Computer Vision,
Li, Z., Meunier, D., Mollenhauer, M., Gretton, A. (1900). Optimal Rates for Regularized Conditional Mean Embedding Learning. arXiv.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (1900). Optimal Regression on Sets. Presented at: eResearch Domain launch event London, UK.
Szabo, Z., Gretton, A., Póczos, B., Sriperumbudur, B. (1900). Simple Consistent Distribution Regression on Compact Metric Domains. Presented at: UCL-Duke Workshop on Sensing and Analysis of High-Dimensional Data (SAHD-2014) London, UK.
Wood, F., Gasthaus, J., Archambeau, C., James, L., Teh, Y.W. (1900). The Sequence Memoizer. Communications of the Association for Computing Machines,