default search action
Arthur Gretton
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j27]Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. J. Mach. Learn. Res. 25: 181:1-181:51 (2024) - [c111]Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. AISTATS 2024: 3961-3969 - [c110]Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland:
Distributional Bellman Operators over Mean Embeddings. ICML 2024 - [c109]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. ICML 2024 - [i89]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. CoRR abs/2402.08530 (2024) - [i88]Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton:
Practical Kernel Tests of Conditional Independence. CoRR abs/2402.13196 (2024) - [i87]Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. CoRR abs/2403.07442 (2024) - [i86]Alexandre Galashov, Valentin De Bortoli, Arthur Gretton:
Deep MMD Gradient Flow without adversarial training. CoRR abs/2405.06780 (2024) - [i85]Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li:
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. CoRR abs/2405.14778 (2024) - [i84]Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol:
Conditional Bayesian Quadrature. CoRR abs/2406.16530 (2024) - [i83]Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa:
Mind the Graph When Balancing Data for Fairness or Robustness. CoRR abs/2406.17433 (2024) - [i82]Tongzheng Ren, Haotian Sun, Antoine Moulin, Arthur Gretton, Bo Dai:
Spectral Representation for Causal Estimation with Hidden Confounders. CoRR abs/2407.10448 (2024) - [i81]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland:
Foundations of Multivariate Distributional Reinforcement Learning. CoRR abs/2409.00328 (2024) - [i80]Zonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. Sriperumbudur:
(De)-regularized Maximum Mean Discrepancy Gradient Flow. CoRR abs/2409.14980 (2024) - 2023
- [j26]Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton:
MMD Aggregated Two-Sample Test. J. Mach. Learn. Res. 24: 194:1-194:81 (2023) - [c108]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. AISTATS 2023: 9637-9661 - [c107]Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton:
Efficient Conditionally Invariant Representation Learning. ICLR 2023 - [c106]Liyuan Xu, Arthur Gretton:
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. ICLR 2023 - [c105]Jerome Baum, Heishiro Kanagawa, Arthur Gretton:
A Kernel Stein Test of Goodness of Fit for Sequential Models. ICML 2023: 1936-1953 - [c104]Felix Biggs, Antonin Schrab, Arthur Gretton:
MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. NeurIPS 2023 - [c103]Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton:
Fast and scalable score-based kernel calibration tests. UAI 2023: 691-700 - [i79]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) - [i78]Felix Biggs, Antonin Schrab, Arthur Gretton:
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. CoRR abs/2306.08777 (2023) - [i77]William I. Walker, Arthur Gretton, Maneesh Sahani:
Prediction under Latent Subgroup Shifts with High-Dimensional Observations. CoRR abs/2306.13472 (2023) - [i76]Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe:
Nonlinear Meta-Learning Can Guarantee Faster Rates. CoRR abs/2307.10870 (2023) - [i75]Liyuan Xu, Arthur Gretton:
Kernel Single Proxy Control for Deterministic Confounding. CoRR abs/2308.04585 (2023) - [i74]Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. CoRR abs/2312.07186 (2023) - [i73]Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland:
Distributional Bellman Operators over Mean Embeddings. CoRR abs/2312.07358 (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 - [i72]Antonin Schrab, Benjamin Guedj, Arthur Gretton:
KSD Aggregated Goodness-of-fit Test. CoRR abs/2202.00824 (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