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Number of items: 138.

Article

(2013) Smooth operators. 30th International Conference on Machine Learning, ICML 2013 (PART 3) pp. 2221-2229.

(2011) Semi-supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters , 32 (11) pp. 1572-1583. 10.1016/j.patrec.2011.02.011.

(2010) Temporal kernel CCA and its application in multimodal neuronal data analysis. Machine Learning , 79 (1-2) pp. 5-27. 10.1007/s10994-009-5153-3.

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 , Article 22. Green open access
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Borgwardt, KM; Gretton, A; Rasch, MJ; Kriegel, H-P; Schölkopf, B; Smola, AJ; (2006) Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics , 22 (14) e49-e57. 10.1093/bioinformatics/btl242.

Bounliphone, W; Belilovsky, E; Tenenhaus, A; Antonoglou, I; Gretton, A; Blashcko, MB; Fast Non-Parametric Tests of Relative Dependency and Similarity.

Davy, M; Desobry, F; Gretton, A; Doncarli, C; (2006) An online support vector machine for abnormal events detection. Signal Processing , 86 (8) pp. 2009-2025. 10.1016/j.sigpro.2005.09.027.

Davy, M; Gretton, A; Doucet, A; Rayner, PJW; (2002) Optimized support vector machines for nonstationary signal classification. IEEE Signal Processing Letters , 9 (12) pp. 442-445. 10.1109/LSP.2002.806070.

Fukumizu, K; Bach, FR; Gretton, A; (2007) Statistical consistency of kernel canonical correlation analysis. Journal of Machine Learning Research , 8 pp. 361-383.

Fukumizu, K; Song, L; Gretton, A; (2013) Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels. JOURNAL OF MACHINE LEARNING RESEARCH , 14 pp. 3753-3783.

Gretton, A; (1999) Design of Chimes to Produce Consonant, Non-Harmonic Scales. Acoustics Australia , 27 (1)

Gretton, A; Belitski, A; Murayama, Y; Schölkopf, B; Logothetis, N; (2006) The effect of artifacts on dependence measurement in fMRI. Magn Reson Imaging , 24 (4) pp. 401-409. 10.1016/j.mri.2005.12.036.

Gretton, A; Borgwardt, K; Rasch, M; Schoelkopf, B; Smola, A; (2012) A Kernel Two-Sample Test. Journal of Machine Learning Research , 13 pp. 723-773. Green open access
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Gretton, A; Fukumizu, K; Sriperumbudur, B; (2009) Discussion of: Brownian distance covariance. The Annals of Applied Statistics , 3 (4) pp. 1285-1294.

Gretton, A; Fukumizu, K; Sriperumbudur, BK; (2009) Discussion of: Brownian distance covariance. Annals of Applied Statistics , 3 (4) pp. 1285-1294. 10.1214/09-AOAS312E.

Gretton, A; Fukumizu, K; Sriperumbudur, BK; Discussion of: Brownian distance covariance. Annals of Applied Statistics , 3 (4) pp. 1285-1294. 10.1214/09-AOAS312E.

Gretton, A; Gyorfi, L; (2010) Consistent Nonparametric Tests of Independence. Journal of Machine Learning Research , 11 pp. 1391-1423.

Gretton, A; Herbrich, R; Smola, A; Bousquet, O; Schölkopf, B; (2005) Kernel methods for measuring independence. Journal of Machine Learning Research , 6 pp. 2075-2129.

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 pp. 1823-1830.

Kanagawa, M; Nishiyama, Y; Gretton, A; Fukumizu, K; (2016) Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput , 28 (2) pp. 382-444. 10.1162/NECO_a_00806.

Ku, S-P; Gretton, A; Macke, J; Logothetis, NK; (2008) Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. MAGNETIC RESONANCE IMAGING , 26 (7) pp. 1007-1014. 10.1016/j.mri.2008.02.016.

Muandet, K; Sriperumbudur, B; Fukumizu, K; Gretton, A; Schölkopf, B; (2016) Kernel Mean Shrinkage Estimators. Journal of Machine Learning Research , 17 , Article 48. Green open access
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Rasch, MJ; Gretton, A; Murayama, Y; Maass, W; Logothetis, NK; (2008) Inferring spike trains from local field potentials. J Neurophysiol , 99 (3) pp. 1461-1476. 10.1152/jn.00919.2007.

Sejdinovic, D; Sriperumbudur, B; Gretton, A; Fukumizu, K; (2013) EQUIVALENCE OF DISTANCE-BASED AND RKHS-BASED STATISTICS IN HYPOTHESIS TESTING. ANNALS OF STATISTICS , 41 (5) pp. 2263-2291. 10.1214/13-AOS1140.

Shelton, JA; Gasthaus, J; Dai, Z; Lücke, J; Gretton, A; (2017) GP-Select: Accelerating EM using adaptive subspace preselection. Neural Computation , 29 (8) pp. 2177-2202. 10.1162/NECO_a_00982. Green open access
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Shen, H; Jegelka, S; Gretton, A; (2009) Fast Kernel-Based independent component analysis. IEEE Transactions on Signal Processing , 57 (9) pp. 3498-3511. 10.1109/TSP.2009.2022857.

Song, L; Bedo, J; Borgwardt, KM; Gretton, A; Smola, A; (2007) Gene selection via the BAHSIC family of algorithms. Bioinformatics , 23 (13) i490-i498. 10.1093/bioinformatics/btm216.

Sriperumbudur, B; Fukumizu, K; Gretton, A; Hyvärinen, A; Kumar, R; (2017) Density Estimation in Infinite Dimensional Exponential Families. Journal of Machine Learning Research , 18 , Article 57. Green open access
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Sriperumbudur, BK; Gretton, A; Fukumizu, K; Schölkopf, B; Lanckriet, GRG; (2010) Hilbert space embeddings and metrics on probability measures. Journal of Machine Learning Research , 11 pp. 1517-1561.

Weichwald, S; Grosse-Wentrup, M; Gretton, A; (2016) MERLiN: Mixture Effect Recovery in Linear Networks. IEEE Journal of Selected Topics in Signal Processing , 10 (7) pp. 1254-1266. 10.1109/JSTSP.2016.2601144. Green open access
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Book chapter

(2009) Colored maximum variance unfolding. In: UNSPECIFIED

Gretton, A; Borgwardt, KM; Rasch, M; Schölkopf, B; Smola, AJ; (2007) A kernel method for the two-sample-problem. In: UNSPECIFIED (pp. 513-520).

Gretton, A; Borgwardt, KM; Rasch, M; Schölkopf, B; Smola, AJ; (2007) A kernel approach to comparing distributions. In: UNSPECIFIED (pp. 1637-1641).

Gretton, A; Fukumizu, K; Teo, CH; Song, L; Schölkopf, B; Smola, AJ; (2009) A kernel statistical test of independence. In: UNSPECIFIED

Huang, J; Smola, AJ; Gretton, A; Borgwardt, KM; Schölkopf, B; (2007) Correcting sample selection bias by unlabeled data. In: UNSPECIFIED (pp. 601-608).

Proceedings paper

(2013) Hilbert space embeddings of predictive state representations. In: (pp. pp. 92-101).

(2013) B-tests: Low variance kernel two-sample tests. In:

(2012) Hilbert space embeddings of pomdps. In: (pp. pp. 644-653).

(2011) Kernel belief propagation. In: (pp. pp. 707-715).

(2010) Characteristic kernels on structured domains excel in robotics and human action recognition. In: (pp. pp. 264-279).

(2010) Non-parametric estimation of integral probability metrics. In: (pp. pp. 1428-1432).

(2009) Generalized clustering via kernel embeddings. In: (pp. pp. 144-152).

(2009) Kernel measures of conditional dependence. In:

(2009) Kernel measures of independence for non-iid data. In: (pp. pp. 1937-1944).

(2009) Learning taxonomies by dependence maximization. In: (pp. pp. 153-160).

(2009) Detecting the direction of causal time series. In:

(2008) Tailoring density estimation via reproducing kernel moment matching. In: (pp. pp. 992-999).

(2007) Fast kernel ICA using an approximate Newton method. In: (pp. pp. 476-483).

(2005) Statistical convergence of kernel CCA. In: (pp. pp. 387-394).

(2004) Multivariate regression via stiefel manifold constraints. In: (pp. pp. 262-269).

(2003) On-line one-class support vector machines. An application to signal segmentation. In: (pp. pp. 709-712).

(2003) The kernel mutual information. In: (pp. pp. 880-883).

(2001) Nonstationary signal classification using support vector machines. In: (pp. pp. 305-308).

Arbel, M; Gretton, AL; (2018) Kernel Conditional Exponential Family. In: Proceedings of the 21st International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2018. (pp. pp. 1337-1346). PMLR Green open access
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Bińkowski, M; Sutherland, DJ; Arbel, M; Gretton, A; (2018) Demystifying MMD GANs. In: Bengio, Yoshua and LeCun, Yann, (eds.) Proceedings of ICLR 2018 : International Conference on Learning Representations. ICLR: Vancouver, BC, Canada,. Green open access
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Blaschko, MB; Lampert, CH; Gretton, A; (2008) Semi-supervised Laplacian regularization of Kernel canonical correlation analysis. In: (pp. pp. 133-145).

Borgwardt, K; Yan, X; Thoma, M; Cheng, H; Gretton, A; Song, L; Smola, A; ... Kriegel, H-P; + view all (2008) Combining near-optimal feature selection with gSpan. In:

Bounliphone, W; Belilovsky, E; Blaschko, MB; Antonoglou, I; Gretton, A; A Test of Relative Similarity For Model Selection in Generative Models. In:

Chwialkowski, K; Gretton, A; A Kernel Independence Test for Random Processes. In:

Chwialkowski, K; Strathmann, H; Gretton, A; (2016) A Kernel Test of Goodness of Fit. In: ICML ’16: Proceedings of the 32nd International Conference on Machine Learning. (pp. pp. 2606-2615). JMLR: Workshop and Conference Proceedings Green open access
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Fukumizu, K; Song, L; Gretton, A; (2011) Kernel Bayes' rule. In:

Fukumizu, K; Song, L; Gretton, A; (2011) Kernel Bayes’ Rule. In:

Fukumizu, K; Sriperumbudur, B; Gretton, A; Schölkopf, B; (2009) Characteristic kernels on groups and semigroups. In: (pp. pp. 473-480).

Gonzalez, JE; Low, Y; Gretton, A; Guestrin, C; (2011) Parallel Gibbs sampling: From colored fields to thin junction trees. In: (pp. pp. 324-332).

Gretton, A; Bousquet, O; Smola, A; Schoelkopf, B; (2005) Measuring Statistical Dependence with Hilbert-Schmidt Norms. In: Springer-Verlag: Berlin/Heidelberg.

Gretton, A; Doucet, A; Herbrich, R; Rayner, PJW; Schölkopf, B; (2001) Support vector regression for black-box system identification. In: (pp. pp. 341-344).

Gretton, A; Fukumizu, K; Harchaoui, Z; Sriperumbudur, BK; (2009) A fast, consistent kernel two-sample test. In: (pp. pp. 673-681).

Gretton, A; Györfi, L; (2008) Nonparametric independence tests: Space partitioning and Kernel approaches. In: (pp. pp. 183-198).

Gretton, A; Smola, A; Huang, J; Schmittfull, M; Borgwardt, K; Schoelkopf, B; (2008) Dataset Shift in Machine Learning. In: nonero-Candela, JQ and Sugiyama, M and Schwaighofer, A and Lawrence, N, (eds.) (pp. pp. 131-160). MIT Press: Cambridge, MA.

Gretton, A; Sriperumbudur, B; Sejdinovic, D; Strathmann, H; Balakrishnan, S; Pontil, M; Fukumizu, K; (2012) Optimal kernel choice for large-scale two-sample tests. In: (pp. pp. 1205-1213).

Grünewälder, S; Lever, G; Baldassarre, L; Pontil, M; Gretton, A; (2012) Modelling transition dynamics in MDPs with RKHS embeddings. In: (pp. pp. 535-542).

Jegelka, S; Gretton, A; (2007) Brisk Kernel ICA. In: Bottou, L and Chapelle, O and DeCoste, D and Weston, J, (eds.) (pp. pp. 225-250). MIT Press

Jitkrittum, W; Gretton, A; Heess, N; Eslami, A; Lakshminarayanan, B; Sejdinovic, D; Szabo, Z; (2015) Just-In-Time Kernel Regression for Expectation Propagation. In: Proceeding of Large-Scale Kernel Learning: Challenges and New Opportunities workshop. : Lille, France. (In press).

Jitkrittum, W; Gretton, A; Heess, N; Eslami, SMA; Lakshminarayanan, B; Sejdinovic, D; Szabó, Z; (2015) Kernel-based just-in-time learning for passing expectation propagation messages. In: Meila, Marina and Heskes, Tom, (eds.) Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 ). (pp. pp. 405-414). AUAI Press: Virginia, USA. Green open access
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Jitkrittum, W; Szabo, Z; Chwialkowski, K; Gretton, A; (2016) Distinguishing distributions with interpretable features. In: ICML 2016 Workshop on Data-Efficient Machine Learning. : New York, USA. Green open access
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Jitkrittum, W; Szabo, Z; Gretton, A; (2017) An Adaptive Test of Independence with Analytic Kernel Embeddings. In: Proceedings of ICML 2017. (pp. pp. 1742-1751). JMLR: Sydney, Australia. Green open access
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Jitkrittum, W; Xu, W; Szabó, Z; Fukumizu, K; Gretton, A; (2017) A linear-time kernel goodness-of-fit test. In: Guyon, I and Luxburg, U.V. and Bengio, S and Wallach and, H and Furges, R and Vishwanathan, S and Garnett., R, (eds.) Proceedings of Advances in Neural Information Processing Systems 30 (NIPS 2017). NIPS Foundation: CA, USA. (In press). Green open access
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Muandet, K; Fukumizu, K; Sriperumbudur, B; Gretton, A; Schölkopf, B; Kernel Mean Estimation and Stein's Effect. In:

Peters, J; Janzing, D; Gretton, A; Schölkopf, B; (2010) Kernel methods for detecting the direction of time series. In: (pp. pp. 57-66).

Rubenstein, PK; Chwialkowski, KP; Gretton, A; (2016) A Kernel Test for Three-Variable Interactions with Random Processes. In: UAI ’16: Proceedings of the 32nd International Conference on Uncertainty in Artificial Intelligence. (pp. pp. 637-646). AUAI Press Green open access
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Schölkopf, B; Logothetis, N; (2005) Kernel constrained covariance for dependence measurement. In: (pp. pp. 112-119).

Sejdinovic, D; Garcia, ML; Strathmann, H; Andrieu, C; Gretton, A; (2013) Kernel Adaptive Metropolis-Hastings. In:

Sejdinovic, D; Gretton, A; Bergsma, W; (2013) A kernel test for three-variable interactions. In:

Smola, A; Gretton, A; Song, L; Schölkopf, B; (2007) A hilbert space embedding for distributions. In: (pp. pp. 13-31).

Song, L; Gretton, A; Guestrin, C; (2010) Nonparametric Tree Graphical Models. In: Teh, YW and Titterington, DM, (eds.) (pp. pp. 765-772). JMLR.org

Song, L; Smola, A; Gretton, A; Borgwardt, KM; (2007) A dependence maximization view of clustering. In: (pp. pp. 815-822).

Song, L; Smola, A; Gretton, A; Borgwardt, KM; Bedo, J; (2007) Supervised feature selection via dependence estimation. In: (pp. pp. 823-830).

Sriperumbudur, BK; Fukumizu, K; Gretton, A; Lanckriet, GRG; Schölkopf, B; (2009) Kernel choice and classifiability for RKHS embeddings of probability distributions. In: (pp. pp. 1750-1758).

Sriperumbudur, BK; Gretton, A; Fukumizu, K; Lanckriet, G; Schölkopf, B; (2008) Injective hilbert space embeddings of probability measures. In: (pp. pp. 111-122).

Strathmann, H; Sejdinovic, D; Livingstone, S; Szabo, Z; Gretton, A; (2015) Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. In: Cortes, C and Lawrence, ND and Lee, DD and Sugiyama, M and Garnett, R, (eds.) Advances in Neural Information Processing Systems 28 (NIPS 2015). NIPS Proceedings Green open access
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Sutherland, DJ; Strathmann, H; Arbel, M; Gretton, A; Efficient and principled score estimation with Nyström kernel exponential families. In: Lawrence, Neil and Reid, Mark, (eds.) Proceedings International Conference on Artificial Intelligence and Statistics - 2018. Proceedings of Machine Learning Research: Playa Blanca, Lanzarote, Canary Islands. Green open access
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Sutherland, DJ; Tung, H-Y; Strathmann, H; De, S; Ramdas, A; Smola, A; Gretton, A; Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. In: Proceedings of the 5th International Conference on Learning Representations (ICLR 2017). International Conference on Learning Representations: Toulon, France. Green open access
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Szabo, Z; Gretton, A; Poczos, B; Sriperumbudur, B; (2015) Two-stage Sampled Learning Theory on Distributions. In: Lebanon, G and Vishwanathan, SVN, (eds.) Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. (pp. pp. 948-957). Journal of Machine Learning Research: San Diego, CA, USA. Green open access
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Thoma, M; Cheng, H; Gretton, A; Han, I; Kriegel, HP; Smola, A; Song, E; ... Borgwardt, K; + view all (2009) Near-optimal supervised feature selection among frequent subgraphs. In: (pp. pp. 1069-1080).

Tillman, RE; Gretton, A; Spirtes, P; (2009) Nonlinear directed acyclic structure learning with weakly additive noise models. In: (pp. pp. 1847-1855).

Weichwald, S; Gretton, A; Schölkopf, B; Grosse-Wentrup, M; (2016) Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. In: PRNI 2016: 6th International Workshop on Pattern Recognition in Neuroimaging. Institute of Electrical and Electronic Engineers (IEEE) Green open access
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Zaremba, W; Gretton, A; Blaschko, M; (2013) B-test: A Non-parametric, Low Variance Kernel Two-sample Test. In: (pp. pp. 755-763). Curran Associates Inc.

Zhou, D; Weston, J; Gretton, A; Bousquet, O; Schoelkopf, B; (2004) Ranking on Data Manifolds. In: Thrun, S and Saul, L and Schoelkopf, B, (eds.) (pp. pp. 169-176). MIT Press: Cambridge, MA, USA.

Report

UNSPECIFIED (Ed). (2011) Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions.

BakIr, G; Gretton, A; Franz, M; Schoelkopf, B; (2004) Multivariate Regression with Stiefel Constraints.

Blaschko, M; Gretton, A; (2008) Taxonomy Inference Using Kernel Dependence Measures.

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

Gretton, A; A simpler condition for consistency of a kernel independence test.

Gretton, A; Borgwardt, K; Rasch, M; Schoelkopf, B; Smola, A; (2008) A Kernel Method for the Two Sample Problem.

Gretton, A; Bousquet, O; Smola, A; Scḧlkopf, B; (2005) Measuring statistical dependence with Hilbert-Schmidt norms.

Gretton, A; Györfi, L; (2010) Consistent nonparametric tests of independence.

Gretton, A; Herbrich, R; Chapelle, O; Schoelkopf, B; Rayner, P; (2001) Estimating the Leave-One-Out Error for Classification Learning with SVMs.

Gretton, A; Herbrich, R; Smola, A; (2003) The Kernel Mutual Information.

Gretton, A; Smola, A; Bousquet, O; Herbrich, R; Schoelkopf, B; Logothetis, N; (2004) Behaviour and Convergence of the Constrained Covariance.

Grünewälder, S; Lever, G; Baldassarre, L; Patterson, S; Gretton, A; Pontil, M; (2012) Conditional mean embeddings as regressors - supplementary.

Jitkrittum, W; Gretton, A; Heess, N; Passing Expectation Propagation Messages with Kernel Methods.

Nishiyama, Y; Kanagawa, M; Gretton, A; Fukumizu, K; Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models.

Sejdinovic, D; Gretton, A; Sriperumbudur, B; Fukumizu, K; (2012) Hypothesis testing using pairwise distances and associated kernels.

Shen, H; Jegelka, S; Gretton, A; (2006) Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function.

Sriperumbudur, BK; Fukumizu, K; Gretton, A; Schölkopf, B; Lanckriet, GRG; On integral probability metrics, φ-divergences and binary classification.

Zhang, Q; Filippi, S; Gretton, A; Sejdinovic, D; (2018) Large-scale kernel methods for independence testing.

Zhou, D; Weston, J; Gretton, A; Bousquet, O; Schölkopf, B; (2004) Ranking on data manifolds.

Conference item

Strathmann, H; Sejdinovic, D; Livingston, S; Schuster, I; Lomeli Garcia, M; Szabo, Z; Andrieu, C; (2016) Kernel techniques for adaptive Monte Carlo methods. Presented at: Greek Stochastics Workshop on Big Data and Big Models, Tinos, Greek. Green open access
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Strathmann, H; Sejdinovic, D; Livingston, S; Szabo, Z; Gretton, A; (2016) Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. Presented at: Theory of Big Data Workshop, London, United Kingdom.

Szabo, Z; Gretton, A; Poczos, B; Sriperumbudur, B; (2014) Vector-valued distribution regression: a simple and consistent approach. Presented at: Statistical Science Seminars, London, UK. Green open access
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Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2015) Consistent Vector-valued Distribution Regression. Presented at: UCL Workshop on the Theory of Big Data, London, UK. Green open access
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Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Distribution Regression - the Set Kernel Heuristic is Consistent. Presented at: CSML Lunch Talk Series, London, UK. Green open access
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Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Learning on Distributions. Presented at: Kernel methods for big data workshop, Lille, France. Green open access
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Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) Consistent Distribution Regression via Mean Embedding. Presented at: University of Hertfordshire, Computer Science Research Colloquium, Hatfield, UK. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2016) Distribution Regression with Minimax-Optimal Guarantee. Presented at: MASCOT-NUM 2016, Toulouse, France. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Learning Theory for Vector-Valued Distribution Regression. Presented at: CMStatistics 2015, London, United Kingdom. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Distribution Regression: Computational and Statistical Tradeoffs. Presented at: CSML Lunch Talk Series, London, United Kingdom. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Distribution Regression: Computational and Statistical Tradeoffs. Presented at: Talk at Princeton University, Princeton, New Jersey. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Regression on Probability Measures: A Simple and Consistent Algorithm. Presented at: CRiSM Seminars, Department of Statistics, University of Warwick, Coventry, United Kingdom. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Vector-valued Distribution Regression - Keep It Simple and Consistent. Presented at: CSML reading group, Department of Statistics, University of Oxford, Oxford, United Kingdom. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) A Simple and Consistent Technique for Vector-valued Distribution Regression. Presented at: Invited talk at the Artificial Intelligence and Natural Computation seminars, University of Birmingham, UK. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Consistent Vector-valued Regression on Probability Measures. Presented at: Invited talk at Prof. Bernhard Schölkopf's lab, Tübingen. Green open access
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Poster

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. Presented at: Data, Learning and Inference workshop (DALI), La Palma (Canaries, Spain). Green open access
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Jitkrittum, W; Gretton, AL; Heess, N; Eslami, SMA; Lakshminarayanan, B; Sejdinovic, D; Szabó, Z; (2015) 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. Green open access
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Szabo, Z; Gretton, A; Póczos, B; Sriperumbudur, B; (2014) 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. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2016) Optimal Regression on Sets. Presented at: eResearch Domain launch event, London, United Kingdom. Green open access
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Szabo, Z; Sriperumbudur, B; Poczos, B; Gretton, A; (2015) Distribution Regression - Make It Simple and Consistent. Presented at: Data, Learning and Inference workshop (DALI), La Palma (Canaries, Spain). Green open access
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Thesis

Gretton, A; (2003) Kernel Methods for Classification and Signal Separation (PhD thesis). Doctoral thesis , UNSPECIFIED.

This list was generated on Sun Apr 21 16:53:40 2019 BST.