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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 - [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]Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Learning Theory for Distribution Regression. CoRR abs/1411.2066 (2014) - [i14]Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton:
GP-select: Accelerating EM using adaptive subspace preselection. CoRR abs/1412.3411 (2014) - 2013
- [j14]Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' rule: Bayesian inference with positive definite kernels. J. Mach. Learn. Res. 14(1): 3753-3783 (2013) - [j13]Le Song, Kenji Fukumizu, Arthur Gretton:
Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. IEEE Signal Process. Mag. 30(4): 98-111 (2013) - [c50]Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor:
Smooth Operators. ICML (3) 2013: 1184-1192 - [c49]Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko:
B-test: A Non-parametric, Low Variance Kernel Two-sample Test. NIPS 2013: 755-763 - [c48]Dino Sejdinovic, Arthur Gretton, Wicher Bergsma:
A Kernel Test for Three-Variable Interactions. NIPS 2013: 1124-1132 - [c47]Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton:
Taxonomic Prediction with Tree-Structured Covariances. ECML/PKDD (2) 2013: 304-319 - [c46]Byron Boots, Geoffrey J. Gordon, Arthur Gretton:
Hilbert Space Embeddings of Predictive State Representations. UAI 2013 - [i13]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein's Effect. CoRR abs/1306.0842 (2013) - [i12]Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko:
B-test: A Non-parametric, Low Variance Kernel Two-sample Test. CoRR abs/1307.1954 (2013) - [i11]Dino Sejdinovic, Maria Lomeli Garcia, Heiko Strathmann, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. CoRR abs/1307.5302 (2013) - [i10]Byron Boots, Geoffrey J. Gordon, Arthur Gretton:
Hilbert Space Embeddings of Predictive State Representations. CoRR abs/1309.6819 (2013) - 2012
- [j12]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test. J. Mach. Learn. Res. 13: 723-773 (2012) - [j11]Le Song, Alexander J. Smola, Arthur Gretton, Justin Bedo, Karsten M. Borgwardt:
Feature Selection via Dependence Maximization. J. Mach. Learn. Res. 13: 1393-1434 (2012) - [c45]Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton:
Modelling transition dynamics in MDPs with RKHS embeddings. ICML 2012 - [c44]Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil:
Conditional mean embeddings as regressors. ICML 2012 - [c43]Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu:
Hypothesis testing using pairwise distances and associated kernels. ICML 2012 - [c42]Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu:
Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222 - [c41]Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu:
Hilbert Space Embeddings of POMDPs. UAI 2012: 644-653 - [i9]Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu:
Hypothesis testing using pairwise distances and associated kernels (with Appendix). CoRR abs/1205.0411 (2012) - [i8]Steffen Grünewälder, Guy Lever, Luca Baldassarre, Sam Patterson, Arthur Gretton, Massimiliano Pontil:
Conditional mean embeddings as regressors - supplementary. CoRR abs/1205.4656 (2012) - [i7]Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton:
Modelling transition dynamics in MDPs with RKHS embeddings. CoRR abs/1206.4655 (2012) - [i6]Dino Sejdinovic, Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu:
Equivalence of distance-based and RKHS-based statistics in hypothesis testing. CoRR abs/1207.6076 (2012) - [i5]Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu:
Hilbert Space Embeddings of POMDPs. CoRR abs/1210.4887 (2012) - 2011
- [j10]Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels, Christoph H. Lampert, Arthur Gretton:
Semi-supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognit. Lett. 32(11): 1572-1583 (2011) - [c40]Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' Rule. NIPS 2011: 1737-1745 - [c39]Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin:
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees. AISTATS 2011: 324-332 - [c38]Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin:
Kernel Belief Propagation. AISTATS 2011: 707-715 - [i4]Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin:
Kernel Belief Propagation. CoRR abs/1105.5592 (2011) - 2010
- [j9]Arthur Gretton, László Györfi:
Consistent Nonparametric Tests of Independence. J. Mach. Learn. Res. 11: 1391-1423 (2010) - [j8]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet:
Hilbert Space Embeddings and Metrics on Probability Measures. J. Mach. Learn. Res. 11: 1517-1561 (2010) - [j7]Felix Bießmann, Frank C. Meinecke, Arthur Gretton, Alexander Rauch, Gregor Rainer, Nikos K. Logothetis, Klaus-Robert Müller:
Temporal kernel CCA and its application in multimodal neuronal data analysis. Mach. Learn. 79(1-2): 5-27 (2010) - [j6]Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt:
Discriminative frequent subgraph mining with optimality guarantees. Stat. Anal. Data Min. 3(5): 302-318 (2010) - [c37]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert R. G. Lanckriet:
Non-parametric estimation of integral probability metrics. ISIT 2010: 1428-1432 - [c36]Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber:
Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. ECML/PKDD (1) 2010: 264-279 - [c35]Le Song, Arthur Gretton, Carlos Guestrin:
Nonparametric Tree Graphical Models. AISTATS 2010: 765-772
2000 – 2009
- 2009
- [j5]Hao Shen, Stefanie Jegelka, Arthur Gretton:
Fast kernel-based independent component analysis. IEEE Trans. Signal Process. 57(9): 3498-3511 (2009) - [c34]Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
Detecting the direction of causal time series. ICML 2009: 801-808 - [c33]Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg:
Generalized Clustering via Kernel Embeddings. KI 2009: 144-152 - [c32]Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur:
A Fast, Consistent Kernel Two-Sample Test. NIPS 2009: 673-681 - [c31]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, Bernhard Schölkopf:
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. NIPS 2009: 1750-1758 - [c30]Robert E. Tillman, Arthur Gretton, Peter Spirtes:
Nonlinear directed acyclic structure learning with weakly additive noise models. NIPS 2009: 1847-1855 - [c29]Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt:
Near-optimal Supervised Feature Selection among Frequent Subgraphs. SDM 2009: 1076-1087 - [i3]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
A note on integral probability metrics and $\phi$-divergences. CoRR abs/0901.2698 (2009) - 2008
- [c28]Arthur Gretton, László Györfi:
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches. ALT 2008: 183-198 - [c27]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
Injective Hilbert Space Embeddings of Probability Measures. COLT 2008: 111-122 - [c26]Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
Kernel Methods for Detecting the Direction of Time Series. GfKl 2008: 57-66 - [c25]Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf:
Tailoring density estimation via reproducing kernel moment matching. ICML 2008: 992-999 - [c24]Matthew B. Blaschko, Arthur Gretton:
Learning Taxonomies by Dependence Maximization. NIPS 2008: 153-160 - [c23]Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Characteristic Kernels on Groups and Semigroups. NIPS 2008: 473-480 - [c22]Xinhua Zhang, Le Song, Arthur Gretton, Alexander J. Smola:
Kernel Measures of Independence for non-iid Data. NIPS 2008: 1937-1944 - [c21]Matthew B. Blaschko, Christoph H. Lampert, Arthur Gretton:
Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. ECML/PKDD (1) 2008: 133-145 - [i2]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample Problem. CoRR abs/0805.2368 (2008) - 2007
- [j4]Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Consistency of Kernel Canonical Correlation Analysis. J. Mach. Learn. Res. 8: 361-383 (2007) - [c20]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641 - [c19]Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. ALT 2007: 13-31 - [c18]Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. Discovery Science 2007: 40-41 - [c17]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt:
A dependence maximization view of clustering. ICML 2007: 815-822 - [c16]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised feature selection via dependence estimation. ICML 2007: 823-830 - [c15]Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola:
Gene selection via the BAHSIC family of algorithms. ISMB/ECCB (Supplement of Bioinformatics) 2007: 490-498 - [c14]Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf:
Kernel Measures of Conditional Dependence. NIPS 2007: 489-496 - [c13]Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Statistical Test of Independence. NIPS 2007: 585-592 - [c12]Le Song, Alexander J. Smola, Karsten M. Borgwardt, Arthur Gretton:
Colored Maximum Variance Unfolding. NIPS 2007: 1385-1392 - [c11]Hao Shen, Stefanie Jegelka, Arthur Gretton:
Fast Kernel ICA using an Approximate Newton Method. AISTATS 2007: 476-483 - [i1]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised Feature Selection via Dependence Estimation. CoRR abs/0704.2668 (2007) - 2006
- [j3]Manuel Davy, Frédéric Desobry, Arthur Gretton, Christian Doncarli:
An online support vector machine for abnormal events detection. Signal Process. 86(8): 2009-2025 (2006) - [c10]Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola:
Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57 - [c9]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520 - [c8]Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf:
Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608 - 2005
- [j2]Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf:
Kernel Methods for Measuring Independence. J. Mach. Learn. Res. 6: 2075-2129 (2005) - [c7]Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis:
Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005: 112-119 - [c6]Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf:
Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77 - [c5]Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Convergence of Kernel CCA. NIPS 2005: 387-394 - 2004
- [c4]Gökhan H. Bakir, Arthur Gretton, Matthias O. Franz, Bernhard Schölkopf:
Multivariate Regression via Stiefel Manifold Constraints. DAGM-Symposium 2004: 262-269 - 2003
- [c3]Arthur Gretton, Frédéric Desobry:
On-line one-class support vector machines. An application to signal segmentation. ICASSP (2) 2003: 709-712 - [c2]Arthur Gretton, Ralf Herbrich, Alexander J. Smola:
The kernel mutual information. ICASSP (4) 2003: 880-884 - [c1]Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf:
Ranking on Data Manifolds. NIPS 2003: 169-176 - 2002
- [j1]Manuel Davy, Arthur Gretton, Arnaud Doucet, Peter J. W. Rayner:
Optimized support vector machines for nonstationary signal classification. IEEE Signal Process. Lett. 9(12): 442-445 (2002)
Coauthor Index
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