2023

Jo, R., Gretton, A. (2023). A Neural Mean Embedding Approach for Back-door and Front-door Adjustment.
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.
Schrab, A., Guedj, B., Gretton, A. (2023). KSD Aggregated Goodness-of-fit Test.
Li, Z., Meunier, D., Mollenhauer, M., Gretton, A. (2023). Optimal Rates for Regularized Conditional Mean Embedding Learning.
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,

2022

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.
Zhu, Y., Gultchin, L., Gretton, A., Kusner, M., Silva, R. (2022). Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach.
Kanagawa, H., Gretton, A., Mackey, L. (2022). Controlling Moments with Kernel Stein Discrepancies.
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.
Jo, R., Chen, Y., Doucet, A., Gretton, A. (2022). Importance Weighting Approach in Kernel Bayes' Rule.
Glaser, P., Arbel, M., Doucet, A., Gretton, A. (2022). Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference.
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.

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
Marx, A., Gretton, A., Mooij, J.M. (2021). A Weaker Faithfulness Assumption based on Triple Interactions.
Jo, R., Kanagawa, H., Gretton, A. (2021). Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation.
Glaser, P., Arbel, M., Gretton, A. (2021). KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support.
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.
Li, Y., Pogodin, R., Sutherland, D.J., Gretton, A. (2021). Self-Supervised Learning with Kernel Dependence Maximization.

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.
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
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.
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

2019

Fernández, T., Gretton, A. (2019). A maximum-mean-discrepancy goodness-of-fit test for censored data.
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
Korshunova, I., Gal, Y., Gretton, A., Dambre, J. (2019). Conditional BRUNO: A neural process for exchangeable labelled data.
Dai, B., Liu, Z., Dai, H., He, N., Gretton, A., Le, S., Schurmaans, D. (2019). Exponential Family Estimation via Adversarial Dynamics Embedding.
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.
Wenliang, L.K., Sutherland, D.J., Strathmann, H., Gretton, A. (2019). Learning deep kernels for exponential family densities.
Arbel, M., Korba, A., Salim, A., Gretton, A. (2019). Maximum Mean Discrepancy Gradient Flow.

2018

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.
Sutherland, D.J., Strathmann, H., Arbel, M., Gretton, A. (2018). Efficient and principled score estimation with Nyström kernel exponential families.
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.
Arbel, M., Sutherland, D.J., Binkowski, M., Gretton, A. (2018). On gradient regularizers for MMD GANs.

2017

Jitkrittum, W., Xu, W., Szabó, Z., Fukumizu, K., Gretton, A. (2017). A linear-time kernel goodness-of-fit test.
Jitkrittum, W., s.z.a.b.o., g.r.e.t.t.o.n. (2017). An Adaptive Test of Independence with Analytic Kernel Embeddings.
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

2016

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.
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.
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.
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
Strathmann, H., Sejdinovic, D., Livingston, S., Szabo, Z., Gretton, A. (2016). Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
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.
Weichwald, S., Gretton, A., Schölkopf, B., Grosse-Wentrup, M. (2016). Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data.

2015

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 .
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.
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.
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Learning Theory for Vector-Valued Distribution Regression.
Jitkrittum, W., Gretton, A., Heess, N. (2015). Passing Expectation Propagation Messages with Kernel Methods. .
Szabo, Z., Sriperumbudur, B., Poczos, B., Gretton, A. (2015). Regression on Probability Measures: A Simple and Consistent Algorithm.
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.
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.
Kanagawa, M., Nishiyama, Y., Gretton, A., Fukumizu, K. (2014). Monte Carlo Filtering Using Kernel Embedding of Distributions.
Szabo, Z., Gretton, A., Poczos, B., Sriperumbudur, B. (2014). Vector-valued Distribution Regression: A Simple and Consistent Approach.

2013

Sejdinovic, D., Gretton, A., Bergsma, W. (2013). A Kernel Test for Three-Variable Interactions.
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.
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.
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
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.
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.
Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K. (2012). Feature Selection via Dependence Maximization. JOURNAL OF MACHINE LEARNING RESEARCH, 13 1393-1434.
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). .
Grunewalder, S., Lever, G., Baldassarre, L., Pontil, M., Gretton, A. (2012). Modelling transition dynamics in MDPs with RKHS embeddings.
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.

2011

Fukumizu, K., Song, L., Gretton, A. (2011). Kernel Bayes’ Rule.
Song, L., Gretton, A., Bickson, D., Low, Y., Guestrin, C. (2011). Kernel Belief Propagation.
Grunewalder, S., Lever, G., Baldassarre, L., Pontil, M., Gretton, A. (2011). Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions. .
Gonzalez, J., Low, Y., Gretton, A., Guestrin, C. (2011). Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees.
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.

2010

Danafar, S., Gretton, A., Schmidhuber, J. (2010). Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition.
Gretton, A., Gyorfi, L. (2010). Consistent Nonparametric Tests of Independence. Journal of Machine Learning Research, 11 1391-1423.
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.
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.
Fukumizu, K., Song, L., Gretton, A. (2010). Kernel Bayes’ Rule.
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.

2009

Gretton, A., Fukumizu, K., Harchaoui, Z., Sriperumbudur, B. (2009). A Fast, Consistent Kernel Two-Sample Test.
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. .
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.
Shen, H., Jegelka, S., Gretton, A. (2009). Fast Kernel-Based Independent Component Analysis. IEEE Transactions on Signal Processing, 57 3498-3511.
Jegelka, S., Gretton, A., Schoelkopf, B., Sriperumbudur, B., Luxburg, U.V. (2009). Generalized clustering via kernel embeddings.
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.
Blaschko, M., Gretton, A. (2009). Learning Taxonomies by Dependence Maximization.
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.
Sriperumbudur, B., Fukumizu, K., A, G., Schoelkopf, B., Lanckriet, G. (2009). On integral probability metrics, φ-divergences and binary classification. .
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,

2008

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.
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
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schoelkopf, B. (2008). Dataset Shift in Machine Learning.
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.
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.
Gretton, A., Gyorfi, L. (2008). Nonparametric Independence Tests: Space Partitioning and Kernel Approaches.
Blaschko, M., Lampert, C., Gretton, A. (2008). Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis.
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. .

2007

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.
Jegelka, S., Gretton, A. (2007). Brisk Kernel ICA.
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.
Fukumizu, K., Bach, F., Gretton, A. (2007). Statistical Consistency of Kernel Canonical Correlation Analysis. J. Mach. Learn. Res., 8 361-383.
Song, L., Smola, A., Gretton, A., Borgwardt, K., Bedo, J. (2007). Supervised Feature Selection via Dependence Estimation.

2006

Davy, M., Desobry, F., Gretton, A., Doncarli, C. (2006). An Online Support Vector Machine for Abnormal Events Detection. Signal Processing, 86 2009-2025.
Shen, H., Jegelka, S., Gretton, A. (2006). Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function. .
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.
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.

2005

Fukumizu, K., Bach, F., Gretton, A. (2005). Consistency of Kernel Canonical Correlation Analysis. .
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.
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.

2004

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Schoelkopf, B., Logothetis, N. (2004). Behaviour and Convergence of the Constrained Covariance. .
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. .
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schoelkopf, B. (2004). Ranking on Data Manifolds.

2003

Gretton, A., Desobry, F. (2003). On-Line One-Class Support Vector Machines: An Application to Signal Segmentation..
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schoelkopf, B. (2003). Ranking on Data Manifolds. .
Gretton, A., Herbrich, R., Smola, A. (2003). The Kernel Mutual Information. .
Gretton, A., Herbrich, R., Smola, A. (2003). The Kernel Mutual Information.

2002

Davy, M., Gretton, A., Doucet, A., Rayner, P. (2002). Optimized Support Vector Machines for Nonstationary Signal Classification. IEEE Signal Processing Letters, 9 442-445.

2001

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.
Gretton, A., Doucet, A., Herbrich, R., Rayner, P., Schoelkopf, B. (2001). Support Vector Regression for Black-Box System Identification.

1999

Gretton, A. (1999). Design of Chimes to Produce Consonant, Non-Harmonic Scales. Acoustics Australia, 27

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.
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.
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.
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.