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2020 – today
- 2023
[i74]Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner, Arthur Gretton, Philipp Berens:
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images. CoRR abs/2303.04862 (2023)- 2022
- [j25]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. J. Mach. Learn. Res. 23: 302:1-302:40 (2022) - [c102]Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy:
Deep Layer-wise Networks Have Closed-Form Weights. AISTATS 2022: 188-225 - [c101]Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton:
Importance Weighted Kernel Bayes' Rule. ICML 2022: 24524-24538 - [c100]Lisa M. Koch, Christian M. Schürch, Arthur Gretton, Philipp Berens:
Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. MIDL 2022: 726-740 - [c99]Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Optimal Rates for Regularized Conditional Mean Embedding Learning. NeurIPS 2022 - [c98]Antonin Schrab, Benjamin Guedj, Arthur Gretton:
KSD Aggregated Goodness-of-fit Test. NeurIPS 2022 - [c97]Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton:
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. NeurIPS 2022 - [c96]Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI 2022: 2414-2424 - [i73]Antonin Schrab
, Benjamin Guedj, Arthur Gretton:
KSD Aggregated Goodness-of-fit Test. CoRR abs/2202.00824 (2022) - [i72]Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy:
Deep Layer-wise Networks Have Closed-Form Weights. CoRR abs/2202.01210 (2022) - [i71]Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton:
Importance Weighting Approach in Kernel Bayes' Rule. CoRR abs/2202.02474 (2022) - [i70]Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR abs/2206.09186 (2022) - [i69]Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton:
Efficient Aggregated Kernel Tests using Incomplete U-statistics. CoRR abs/2206.09194 (2022) - [i68]Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton:
Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR abs/2206.11142 (2022) - [i67]Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Optimal Rates for Regularized Conditional Mean Embedding Learning. CoRR abs/2208.01711 (2022) - [i66]Liyuan Xu, Arthur Gretton:
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. CoRR abs/2210.06610 (2022) - [i65]Jerome Baum, Heishiro Kanagawa, Arthur Gretton:
A kernel Stein test of goodness of fit for sequential models. CoRR abs/2210.10741 (2022) - [i64]Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton:
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. CoRR abs/2210.14756 (2022) - [i63]Heishiro Kanagawa, Arthur Gretton, Lester Mackey:
Controlling Moments with Kernel Stein Discrepancies. CoRR abs/2211.05408 (2022) - [i62]Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton:
Efficient Conditionally Invariant Representation Learning. CoRR abs/2212.08645 (2022) - [i61]Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai:
Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR abs/2212.11254 (2022) - 2021
- [c95]Michael Arbel, Liang Zhou, Arthur Gretton:
Generalized Energy Based Models. ICLR 2021 - [c94]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR 2021 - [c93]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. ICLR 2021 - [c92]Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet:
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML 2021: 7512-7523 - [c91]Pierre Glaser, Michael Arbel, Arthur Gretton:
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. NeurIPS 2021: 8018-8031 - [c90]Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton:
Self-Supervised Learning with Kernel Dependence Maximization. NeurIPS 2021: 15543-15556 - [c89]Liyuan Xu, Heishiro Kanagawa, Arthur Gretton:
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. NeurIPS 2021: 26264-26275 - [c88]Alexander Marx, Arthur Gretton, Joris M. Mooij:
A weaker faithfulness assumption based on triple interactions. UAI 2021: 451-460 - [i60]Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet:
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR abs/2105.04544 (2021) - [i59]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. CoRR abs/2105.10148 (2021) - [i58]Zhu Li, Zhi-Hua Zhou, Arthur Gretton:
Towards an Understanding of Benign Overfitting in Neural Networks. CoRR abs/2106.03212 (2021) - [i57]Liyuan Xu, Heishiro Kanagawa, Arthur Gretton:
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. CoRR abs/2106.03907 (2021) - [i56]Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton:
Self-Supervised Learning with Kernel Dependence Maximization. CoRR abs/2106.08320 (2021) - [i55]Pierre Glaser, Michael Arbel, Arthur Gretton:
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. CoRR abs/2106.08929 (2021) - [i54]Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton:
MMD Aggregated Two-Sample Test. CoRR abs/2110.15073 (2021) - [i53]Rahul Singh, Liyuan Xu, Arthur Gretton:
Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects. CoRR abs/2111.03950 (2021) - [i52]Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol:
Composite Goodness-of-fit Tests with Kernels. CoRR abs/2111.10275 (2021) - 2020
- [j24]Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre:
Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing 416: 305-309 (2020) - [j23]Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu:
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. 109(5): 939-972 (2020) - [c87]Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed:
A case for new neural network smoothness constraints. ICBINB@NeurIPS 2020: 21-32 - [c86]Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar:
Kernelized Wasserstein Natural Gradient. ICLR 2020 - [c85]Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton:
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. ICML 2020: 3112-3122 - [c84]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML 2020: 6316-6326 - [c83]Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton:
A kernel test for quasi-independence. NeurIPS 2020 - [c82]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS 2020 - [i51]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR abs/2002.09116 (2020) - [i50]Michael Arbel, Liang Zhou, Arthur Gretton:
KALE: When Energy-Based Learning Meets Adversarial Training. CoRR abs/2003.05033 (2020) - [i49]Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy:
Layer-wise Learning of Kernel Dependence Networks. CoRR abs/2006.08539 (2020) - [i48]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR abs/2006.09797 (2020) - [i47]Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton:
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. CoRR abs/2008.08397 (2020) - [i46]Rahul Singh, Liyuan Xu, Arthur Gretton:
Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning. CoRR abs/2010.04855 (2020) - [i45]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR abs/2010.05380 (2020) - [i44]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. CoRR abs/2010.07154 (2020) - [i43]Alexander Marx, Arthur Gretton, Joris M. Mooij:
A Weaker Faithfulness Assumption based on Triple Interactions. CoRR abs/2010.14265 (2020) - [i42]Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy:
Kernel Dependence Network. CoRR abs/2011.03320 (2020) - [i41]Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed:
A case for new neural network smoothness constraints. CoRR abs/2012.07969 (2020)
2010 – 2019
- 2019
- [j22]Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani:
Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. 29(5): 1127-1147 (2019) - [c81]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS 2019: 2321-2330 - [c80]Tamara Fernandez, Arthur Gretton:
A maximum-mean-discrepancy goodness-of-fit test for censored data. AISTATS 2019: 2966-2975 - [c79]Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre:
Conditional BRUNO: a neural process for exchangeable labelled data. ESANN 2019 - [c78]Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton:
Learning deep kernels for exponential family densities. ICML 2019: 6737-6746 - [c77]Rahul Singh, Maneesh Sahani, Arthur Gretton:
Kernel Instrumental Variable Regression. NeurIPS 2019: 4595-4607 - [c76]Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. NeurIPS 2019: 6481-6491 - [c75]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS 2019: 10977-10988 - [i40]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR abs/1904.12083 (2019) - [i39]Rahul Singh, Maneesh Sahani, Arthur Gretton:
Kernel Instrumental Variable Regression. CoRR abs/1906.00232 (2019) - [i38]Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. CoRR abs/1906.04370 (2019) - [i37]Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton:
A Kernel Stein Test for Comparing Latent Variable Models. CoRR abs/1907.00586 (2019) - [i36]Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton:
Counterfactual Distribution Regression for Structured Inference. CoRR abs/1908.07193 (2019) - [i35]Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar:
Kernelized Wasserstein Natural Gradient. CoRR abs/1910.09652 (2019) - 2018
- [j21]Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic:
Large-scale kernel methods for independence testing. Stat. Comput. 28(1): 113-130 (2018) - [c74]Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation with Nyström kernel exponential families. AISTATS 2018: 652-660 - [c73]Michael Arbel, Arthur Gretton:
Kernel Conditional Exponential Family. AISTATS 2018: 1337-1346 - [c72]Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton:
Demystifying MMD GANs. ICLR (Poster) 2018 - [c71]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. NeurIPS 2018: 816-827 - [c70]Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton:
On gradient regularizers for MMD GANs. NeurIPS 2018: 6701-6711 - [c69]Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre:
BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS 2018: 7190-7198 - [i34]Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton:
Demystifying MMD GANs. CoRR abs/1801.01401 (2018) - [i33]Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton:
On gradient regularizers for MMD GANs. CoRR abs/1805.11565 (2018) - [i32]Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani:
Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR abs/1807.00400 (2018) - [i31]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. CoRR abs/1810.11630 (2018) - [i30]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. CoRR abs/1811.02228 (2018) - [i29]Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton:
Learning deep kernels for exponential family densities. CoRR abs/1811.08357 (2018) - 2017
- [j20]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar:
Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. 18: 57:1-57:59 (2017) - [j19]Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton:
GP-Select: Accelerating EM Using Adaptive Subspace Preselection. Neural Comput. 29(8): 2177-2202 (2017) - [c68]Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) 2017 - [c67]Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton:
An Adaptive Test of Independence with Analytic Kernel Embeddings. ICML 2017: 1742-1751 - [c66]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. NIPS 2017: 262-271 - [i28]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. CoRR abs/1705.07673 (2017) - [i27]Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation. CoRR abs/1705.08360 (2017) - 2016
- [j18]Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. 17: 48:1-48:41 (2016) - [j17]Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton:
Learning Theory for Distribution Regression. J. Mach. Learn. Res. 17: 152:1-152:40 (2016) - [j16]Sebastian Weichwald, Moritz Grosse-Wentrup, Arthur Gretton:
MERLiN: Mixture Effect Recovery in Linear Networks. IEEE J. Sel. Top. Signal Process. 10(7): 1254-1266 (2016) - [j15]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. 28(2): 382-444 (2016) - [c65]Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton:
A Kernel Test of Goodness of Fit. ICML 2016: 2606-2615 - [c64]Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton:
Interpretable Distribution Features with Maximum Testing Power. NIPS 2016: 181-189 - [c63]Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. PRNI 2016: 1-4 - [c62]Paul K. Rubenstein, Kacper Chwialkowski, Arthur Gretton:
A Kernel Test for Three-Variable Interactions with Random Processes. UAI 2016 - [c61]Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton:
A Test of Relative Similarity For Model Selection in Generative Models. ICLR (Poster) 2016 - [e1]Arthur Gretton, Christian C. Robert:
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016. JMLR Workshop and Conference Proceedings 51, JMLR.org 2016 [contents] - [i26]Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. CoRR abs/1605.00391 (2016) - [i25]Wittawat Jitkrittum, Zoltán Szabó, Kacper Chwialkowski, Arthur Gretton:
Interpretable Distribution Features with Maximum Testing Power. CoRR abs/1605.06796 (2016) - [i24]Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton:
An Adaptive Test of Independence with Analytic Kernel Embeddings. CoRR abs/1610.04782 (2016) - [i23]Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR abs/1611.04488 (2016) - [i22]Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko:
Fast Non-Parametric Tests of Relative Dependency and Similarity. CoRR abs/1611.05740 (2016) - [i21]Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf:
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). Dagstuhl Reports 6(11): 142-167 (2016) - 2015
- [c60]Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Two-stage sampled learning theory on distributions. AISTATS 2015 - [c59]Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew B. Blaschko:
A low variance consistent test of relative dependency. ICML 2015: 20-29 - [c58]Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton:
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS 2015: 955-963 - [c57]Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton:
Fast Two-Sample Testing with Analytic Representations of Probability Measures. NIPS 2015: 1981-1989 - [c56]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI 2015: 405-414 - [i20]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess:
Passing Expectation Propagation Messages with Kernel Methods. CoRR abs/1501.00375 (2015) - [i19]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR abs/1503.02551 (2015) - 2014
- [c55]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI 2014: 1897-1903 - [c54]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein Effect. ICML 2014: 10-18 - [c53]Kacper Chwialkowski, Arthur Gretton:
A Kernel Independence Test for Random Processes. ICML 2014: 1422-1430 - [c52]Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. ICML 2014: 1665-1673 - [c51]Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton:
A Wild Bootstrap for Degenerate Kernel Tests. NIPS 2014: 3608-3616 - [i18]Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. CoRR abs/1402.1754 (2014) - [i17]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. CoRR abs/1405.5505 (2014) - [i16]Wacha Bounliphone, Arthur Gretton, Matthew B. Blaschko:
A low variance consistent test of relative dependency. CoRR abs/1406.3852 (2014) - [i15]