Browse by UCL people
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Number of items: 86.
Article
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.
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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)
pp. 1-40.
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Fernandez, 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
10.1080/01621459.2021.1961784.
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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.
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Kanagawa, Heishiro;
Jitkrittum, Wittawat;
Mackey, Lester;
Fukumizu, Kenji;
Gretton, Arthur;
(2023)
A kernel Stein test for comparing latent variable models.
Journal of the Royal Statistical Society: Statistical Methodology Series B
, Article qkad050. 10.1093/jrsssb/qkad050.
(In press).
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Korshunova, I;
Gal, Y;
Gretton, A;
Dambre, J;
(2020)
Conditional BRUNO: A neural process for exchangeable labelled data.
Neurocomputing
, 416
pp. 305-309.
10.1016/j.neucom.2019.11.108.
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Lomelí, M;
Rowland, M;
Gretton, A;
Ghahramani, Z;
(2019)
Antithetic and Monte Carlo kernel estimators for partial rankings.
Statistics and Computing
, 29
pp. 1127-1147.
10.1007/s11222-019-09859-z.
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Muandet, K;
Sriperumbudur, B;
Fukumizu, K;
Gretton, A;
Schölkopf, B;
(2016)
Kernel Mean Shrinkage Estimators.
Journal of Machine Learning Research
, 17
, Article 48.
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Nishiyama, Y;
Kanagawa, M;
Gretton, A;
Fukumizu, K;
(2022)
Model-based kernel sum rule: kernel Bayesian inference with probabilistic model.
Machine Learning
, 109
(5)
pp. 939-972.
10.1007/s10994-019-05852-9.
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Schrab, Antonin;
Kim, Ilmun;
Albert, Mélisande;
Laurent, Béatrice;
Guedj, Benjamin;
Gretton, Arthur;
(2023)
MMD Aggregated Two-Sample Test.
Journal of Machine Learning Research (JMLR)
, 24
, Article 194.
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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.
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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.
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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.
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Proceedings paper
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Alabdulmohsin, Ibrahim;
Chiou, Nicole;
D’Amour, Alexander;
Gretton, Arthur;
Koyejo, Sanmi;
Kusner, Matt J;
Pfohl, Stephen R;
... Tsai, Katherine; + view all
(2023)
Adapting to Latent Subgroup Shifts via Concepts and Proxies.
In:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 9637-9661).
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Arbel, M;
Zhou, L;
Gretton, A;
(2021)
Generalized Energy Based Models.
In:
Proceedings of the 9th International Conference on Learning Representations: ICLR 2021.
ICLR
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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
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Arbel, M;
Korba, A;
Salim, A;
Gretton, A;
(2019)
Maximum Mean Discrepancy Gradient Flow.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 32 (NIPS 2019).
NIPS Proceedingsβ: Vancouver, Canada.
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Arbel, M;
Sutherland, DJ;
Bińkowski, M;
Gretton, A;
(2018)
On gradient regularizers for MMD GANs.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 31 (NIPS 2018).
NIPS Proceedings: Montreal, Canada.
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Baume, Jerome;
Kanagawa, Heishiro;
Gretton, Arthur;
(2023)
A Kernel Stein Test of Goodness of Fit for Sequential Models.
In:
Proceedings of the International Conference on Machine Learning.
ICML Proceedings
(In press).
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Biggs, Felix;
Schrab, antonin;
Gretton, Arthur;
(2023)
MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting.
In:
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
(pp. pp. 1-38).
NeurIPS
(In press).
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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,.
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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
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Dai, B;
Dai, H;
Gretton, A;
Song, L;
Schuurmans, D;
He, N;
(2019)
Kernel Exponential Family Estimation via Doubly Dual Embedding.
In: Chaudhuri, K and Sugiyama, M, (eds.)
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics.
(pp. pp. 2321-2330).
Proceedings of Machine Learning Research: Naha, Okinawa, Japan.
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Dai, B;
Liu, Z;
Dai, H;
He, N;
Gretton, A;
Le, S;
Schurmaans, D;
(2019)
Exponential Family Estimation via Adversarial Dynamics Embedding.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 32 (NIPS 2019).
NIPS Proceedingsβ: Vancouver, Canada.
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Fernández, T;
Xu, W;
Ditzhaus, M;
Gretton, A;
(2020)
A kernel test for quasi-independence.
In: Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M.F. and Lin, H., (eds.)
NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems.
Neural Information Processing Systems Conference: Vancouver, Canada.
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Fernández, T;
Gretton, A;
(2019)
A maximum-mean-discrepancy goodness-of-fit test for censored data.
In: Chaudhuri, K and Sugiyama, M, (eds.)
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics.
(pp. pp. 2966-2975).
Proceedings of Machine Learning Research: Naha, Okinawa, Japan.
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Fernandez Aguilar, T;
Gretton, A;
Rivera, N;
XU, W;
(2020)
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data.
In:
Proceedings of the 37th International Conference on Machine Learning.
(pp. pp. 3112-3122).
PMLR: Vienna, Austria.
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Glaser, P;
Arbel, M;
Gretton, A;
(2021)
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support.
In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Wortman Vaughan, J, (eds.)
Advances in Neural Information Processing Systems 34 (NeurIPS 2021).
(pp. pp. 8018-8031).
NeurIPS
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Glaser, P;
Widmann, D;
Lindsten, F;
Gretton, A;
(2023)
Fast and Scalable Score-Based Kernel Calibration Tests.
In:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 691-700).
PMLR: Pittsburgh, PA, USA.
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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).
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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.
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Jitkrittum, W;
Sangkloy, P;
Schölkopf, B;
Kanagawa, H;
Hays, J;
Gretton, A;
(2018)
Informative features for model comparison.
In:
Advances in Neural Information Processing Systems 31 (NIPS 2018).
(pp. pp. 808-819).
Neural Information Processing Systems Foundation, Inc.: Montréal, Canada.
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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.
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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.
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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).
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Koch, LM;
Schürch, CM;
Gretton, A;
Berens, P;
(2022)
Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection.
In:
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning.
(pp. pp. 726-740).
Proceedings of Machine Learning Research (PMLR)
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Korba, A;
Salim, A;
Arbel, M;
Luise, G;
Gretton, A;
(2020)
A Non-Asymptotic Analysis for
Stein Variational Gradient Descent.
In: Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M.F. and Lin, H., (eds.)
NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems.
Neural Information Processing Systems Conference: Vancouver, Canada.
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Korshunova, I;
Degrave, J;
Huszár, F;
Gal, Y;
Gretton, A;
Dambre, J;
(2018)
Bruno: A deep recurrent model for exchangeable data.
In:
Advances in Neural Information Processing Systems 31 (NIPS 2018).
(pp. pp. 7190-7198).
Neural Information Processing Systems Foundation, Inc.: Montréal, Canada.
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Korshunova, I;
Gal, Y;
Gretton, A;
Dambre, J;
(2019)
Conditional BRUNO: A neural process for exchangeable labelled data.
In:
ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
European Symposium on Artificial Neural Networks (ESANN): Bruges, Belgium.
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Li, Y;
Pogodin, R;
Sutherland, DJ;
Gretton, A;
(2021)
Self-Supervised Learning with Kernel Dependence Maximization.
In:
Advances in Neural Information Processing Systems 34 (NeurIPS 2021).
(pp. pp. 15543-15556).
NeurIPS
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Li, Zhu;
Meunier, D;
Mollenhauer, Mattes;
Gretton, A;
(2022)
Optimal Rates for Regularized Conditional Mean Embedding Learning.
In:
NeurIPS Proceedings: Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
NeurIPS
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Liu, F;
Xu, W;
Lu, J;
Zhang, G;
Gretton, A;
Sutherland, DJ;
(2020)
Learning deep kernels for non-parametric two-sample tests.
In:
Proceedings of the 37th International Conference on Machine Learning.
(pp. pp. 6272-6282).
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Marx, A;
Gretton, A;
Mooij, JM;
(2021)
A Weaker Faithfulness Assumption based on Triple Interactions.
In:
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 451-460).
Proceedings of Machine Learning Research: Online conference.
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Mastouri, Afsaneh;
Zhu, Yuchen;
Gultchin, Limor;
Korba, Anna;
Silva, Ricardo;
Kusner, Matt J;
Gretton, Arthur;
(2021)
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction.
In: Meila, M and Zhang, T, (eds.)
Proceedings of the 38 th International Conference on Machine Learning.
(pp. pp. 1-12).
PMLR
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Moskovitz, T;
Arbel, M;
Huszar, F;
Gretton, A;
(2021)
EFFICIENT WASSERSTEIN NATURAL GRADIENTS FOR REINFORCEMENT LEARNING.
In:
Proceedings of the 9th International Conference on Learning Representations: ICLR 2021.
ICLR: Virtual conference.
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Moskovitz, Ted;
Arbel, Michael;
Huszar, Ferenc;
Gretton, Arthur;
(2021)
Efficient Wasserstein Natural Gradients for Reinforcement Learning.
In:
ICLR 2021 - 9th International Conference on Learning Representations.
ICLR
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Pogodin, Roman;
Deka, Namrata;
Li, Yazhe;
Sutherland, Danica J;
Veitch, Victor;
Gretton, Arthur;
(2023)
Efficient Conditionally Invariant Representation Learning.
In:
Proceedings of the Eleventh International Conference on Learning Representations.
(pp. p. 4723).
International Conference on Learning Representations: Kigali, Rwanda.
(In press).
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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
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Schrab, Antonin;
Guedj, Benjamin;
Gretton, Arthur;
(2023)
KSD Aggregated Goodness-of-fit Test.
In:
Proceedings of the Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
NeurIPS
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Schrab, Antonin;
Kim, Ilmun;
Guedj, Benjamin;
Gretton, Arthur;
(2022)
Efficient Aggregated Kernel Tests using Incomplete U-statistics.
In:
NeurIPS Proceedings: Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
NeurIPS
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Singh, R;
Sahani, M;
Gretton, A;
(2019)
Kernel Instrumental Variable Regression.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett., R, (eds.)
Proceedings of Advances in Neural Information Processing Systems 32 (NIPS 2019).
NIPS Proceedings
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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
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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.
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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.
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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.
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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)
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Wenliang, LK;
Sutherland, DJ;
Strathmann, H;
Gretton, A;
(2019)
Learning deep kernels for exponential family densities.
In:
Proceedings of the 36th International Conference on Machine Learning.
(pp. pp. 11693-11710).
Proceedings of Machine Learning Research (PMLR): Long Beach, CA, USA.
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Wu, C;
Masoomi, A;
Gretton, A;
Dy, J;
(2022)
Deep Layer-wise Networks Have Closed-Form Weights.
In:
Proceedings of the 25th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 188-225).
Valencia, Spain
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Xu, L;
Chen, Y;
Srinivasan, S;
de Freitas, N;
Doucet, A;
Gretton, A;
(2021)
Learning Deep Features in Instrumental Variable Regression.
In:
Proceedings of the 9th International Conference on Learning Representations: ICLR 2021.
ICLR
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Xu, Liyuan;
Gretton, Arthur;
(2023)
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment.
In:
Proceedings of the Eleventh International Conference on Learning Representations.
(pp. p. 2756).
International Conference on Learning Representations: Kigali, Rwanda.
(In press).
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Zhu, Y;
Gultchin, L;
Gretton, A;
Kusner, M;
Silva, R;
(2022)
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach.
In: Cussens, J and Zhang, K, (eds.)
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022.
(pp. pp. 2414-2424).
Proceedings of Machine Learning Research (PMLR): Eindhoven, Netherlands.
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Report
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2016)
Learning Theory for Distribution Regression.
Journal of Machine Learning Research: London, UK.
(In press).
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Working / discussion paper
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Interpretable Distribution Features with Maximum Testing Power.
ArXiv
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Xu, Liyuan;
Gretton, Arthur;
(2022)
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment.
arXiv: Ithaca, NY, USA.
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Conference item
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Poster
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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).
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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.
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.
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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.
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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.
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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).
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Thesis
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Gretton, A;
(2003)
Kernel Methods for Classification and Signal Separation (PhD thesis).
Doctoral thesis , UNSPECIFIED.
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