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Author Information
Arthur Gretton (Google Deepmind / UCL)
Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University. Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).
Bharath Sriperumbudur (The Pennsylvania State University)
Dino Sejdinovic (University of Adelaide)
Heiko Strathmann (UCL)
Sivaraman Balakrishnan (CMU)
Massimiliano Pontil (IIT & UCL)
Kenji Fukumizu (Institute of Statistical Mathematics / Preferred Networks / RIKEN AIP)
More from the Same Authors
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2021 : Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects »
Rahul Singh · Ritsugen Jo · Arthur Gretton -
2021 : Composite Goodness-of-fit Tests with Kernels »
Oscar Key · Tamara Fernandez · Arthur Gretton · Francois-Xavier Briol -
2022 : Bayesian inference for aerosol vertical profiles »
Shahine Bouabid · Duncan Watson-Parris · Dino Sejdinovic -
2022 Poster: Optimal Rates for Regularized Conditional Mean Embedding Learning »
Zhu Li · Dimitri Meunier · Mattes Mollenhauer · Arthur Gretton -
2022 Poster: Invariance Learning based on Label Hierarchy »
Shoji Toyota · Kenji Fukumizu -
2022 Poster: KSD Aggregated Goodness-of-fit Test »
Antonin Schrab · Benjamin Guedj · Arthur Gretton -
2022 Poster: Efficient Aggregated Kernel Tests using Incomplete $U$-statistics »
Antonin Schrab · Ilmun Kim · Benjamin Guedj · Arthur Gretton -
2022 Poster: Unsupervised Learning of Equivariant Structure from Sequences »
Takeru Miyato · Masanori Koyama · Kenji Fukumizu -
2022 Poster: Giga-scale Kernel Matrix-Vector Multiplication on GPU »
Robert Hu · Siu Lun Chau · Dino Sejdinovic · Joan Glaunès -
2022 Poster: Explaining Preferences with Shapley Values »
Robert Hu · Siu Lun Chau · Jaime Ferrando Huertas · Dino Sejdinovic -
2022 Poster: RKHS-SHAP: Shapley Values for Kernel Methods »
Siu Lun Chau · Robert Hu · Javier González · Dino Sejdinovic -
2022 Poster: Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning »
Veit David Wild · Robert Hu · Dino Sejdinovic -
2021 Workshop: Machine Learning Meets Econometrics (MLECON) »
David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou -
2021 Poster: KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support »
Pierre Glaser · Michael Arbel · Arthur Gretton -
2021 Poster: Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation »
Ritsugen Jo · Heishiro Kanagawa · Arthur Gretton -
2021 Poster: Self-Supervised Learning with Kernel Dependence Maximization »
Yazhe Li · Roman Pogodin · Danica J. Sutherland · Arthur Gretton -
2021 Poster: BayesIMP: Uncertainty Quantification for Causal Data Fusion »
Siu Lun Chau · Jean-Francois Ton · Javier González · Yee Teh · Dino Sejdinovic -
2021 Poster: Deconditional Downscaling with Gaussian Processes »
Siu Lun Chau · Shahine Bouabid · Dino Sejdinovic -
2020 Poster: A Non-Asymptotic Analysis for Stein Variational Gradient Descent »
Anna Korba · Adil Salim · Michael Arbel · Giulia Luise · Arthur Gretton -
2020 Poster: Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits »
Arya Akhavan · Massimiliano Pontil · Alexandre Tsybakov -
2020 Poster: The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning »
Giulia Denevi · Massimiliano Pontil · Carlo Ciliberto -
2020 Poster: A Unified View of Label Shift Estimation »
Saurabh Garg · Yifan Wu · Sivaraman Balakrishnan · Zachary Lipton -
2020 Poster: Estimating weighted areas under the ROC curve »
Andreas Maurer · Massimiliano Pontil -
2020 Poster: Robust Persistence Diagrams using Reproducing Kernels »
Siddharth Vishwanath · Kenji Fukumizu · Satoshi Kuriki · Bharath Sriperumbudur -
2020 Poster: A kernel test for quasi-independence »
Tamara Fernandez · Wenkai Xu · Marc Ditzhaus · Arthur Gretton -
2020 Spotlight: A kernel test for quasi-independence »
Tamara Fernandez · Wenkai Xu · Marc Ditzhaus · Arthur Gretton -
2019 Poster: Semi-flat minima and saddle points by embedding neural networks to overparameterization »
Kenji Fukumizu · Shoichiro Yamaguchi · Yoh-ichi Mototake · Mirai Tanaka -
2019 Poster: Hyperparameter Learning via Distributional Transfer »
Ho Chung Law · Peilin Zhao · Leung Sing Chan · Junzhou Huang · Dino Sejdinovic -
2019 Poster: Exponential Family Estimation via Adversarial Dynamics Embedding »
Bo Dai · Zhen Liu · Hanjun Dai · Niao He · Arthur Gretton · Le Song · Dale Schuurmans -
2019 Poster: Online-Within-Online Meta-Learning »
Giulia Denevi · Dimitris Stamos · Carlo Ciliberto · Massimiliano Pontil -
2019 Poster: Maximum Mean Discrepancy Gradient Flow »
Michael Arbel · Anna Korba · Adil Salim · Arthur Gretton -
2019 Poster: Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm »
Giulia Luise · Saverio Salzo · Massimiliano Pontil · Carlo Ciliberto -
2019 Spotlight: Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm »
Giulia Luise · Saverio Salzo · Massimiliano Pontil · Carlo Ciliberto -
2019 Poster: Kernel Instrumental Variable Regression »
Rahul Singh · Maneesh Sahani · Arthur Gretton -
2019 Poster: Tree-Sliced Variants of Wasserstein Distances »
Tam Le · Makoto Yamada · Kenji Fukumizu · Marco Cuturi -
2019 Oral: Kernel Instrumental Variable Regression »
Rahul Singh · Maneesh Sahani · Arthur Gretton -
2019 Tutorial: Interpretable Comparison of Distributions and Models »
Wittawat Jitkrittum · Danica J. Sutherland · Arthur Gretton -
2018 Poster: Informative Features for Model Comparison »
Wittawat Jitkrittum · Heishiro Kanagawa · Patsorn Sangkloy · James Hays · Bernhard Schölkopf · Arthur Gretton -
2018 Poster: Bilevel learning of the Group Lasso structure »
Jordan Frecon · Saverio Salzo · Massimiliano Pontil -
2018 Poster: Learning To Learn Around A Common Mean »
Giulia Denevi · Carlo Ciliberto · Dimitris Stamos · Massimiliano Pontil -
2018 Spotlight: Bilevel learning of the Group Lasso structure »
Jordan Frecon · Saverio Salzo · Massimiliano Pontil -
2018 Poster: Causal Inference via Kernel Deviance Measures »
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh -
2018 Poster: BRUNO: A Deep Recurrent Model for Exchangeable Data »
Iryna Korshunova · Jonas Degrave · Ferenc Huszar · Yarin Gal · Arthur Gretton · Joni Dambre -
2018 Spotlight: Causal Inference via Kernel Deviance Measures »
Jovana Mitrovic · Dino Sejdinovic · Yee Whye Teh -
2018 Poster: Variational Learning on Aggregate Outputs with Gaussian Processes »
Ho Chung Law · Dino Sejdinovic · Ewan Cameron · Tim Lucas · Seth Flaxman · Katherine Battle · Kenji Fukumizu -
2018 Poster: Hamiltonian Variational Auto-Encoder »
Anthony Caterini · Arnaud Doucet · Dino Sejdinovic -
2018 Poster: On gradient regularizers for MMD GANs »
Michael Arbel · Danica J. Sutherland · Mikołaj Bińkowski · Arthur Gretton -
2017 : An Efficient Method to Impose Fairness in Linear Models »
Massimiliano Pontil · John Shawe-Taylor -
2017 Workshop: Workshop on Prioritising Online Content »
John Shawe-Taylor · Massimiliano Pontil · Nicolò Cesa-Bianchi · Emine Yilmaz · Chris Watkins · Sebastian Riedel · Marko Grobelnik -
2017 : Conditional Densities and Efficient Models in Infinite Exponential Families »
Arthur Gretton -
2017 : Learning on topological and geometrical structures of data. »
Kenji Fukumizu -
2017 Poster: A Linear-Time Kernel Goodness-of-Fit Test »
Wittawat Jitkrittum · Wenkai Xu · Zoltan Szabo · Kenji Fukumizu · Arthur Gretton -
2017 Oral: A Linear-Time Kernel Goodness-of-Fit Test »
Wittawat Jitkrittum · Wenkai Xu · Zoltan Szabo · Kenji Fukumizu · Arthur Gretton -
2017 Poster: Testing and Learning on Distributions with Symmetric Noise Invariance »
Ho Chung Law · Christopher Yau · Dino Sejdinovic -
2017 Poster: Consistent Multitask Learning with Nonlinear Output Relations »
Carlo Ciliberto · Alessandro Rudi · Lorenzo Rosasco · Massimiliano Pontil -
2017 Poster: Trimmed Density Ratio Estimation »
Song Liu · Akiko Takeda · Taiji Suzuki · Kenji Fukumizu -
2016 Workshop: Adaptive and Scalable Nonparametric Methods in Machine Learning »
Aaditya Ramdas · Arthur Gretton · Bharath Sriperumbudur · Han Liu · John Lafferty · Samory Kpotufe · Zoltán Szabó -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Learning features to distinguish distributions »
Arthur Gretton -
2016 Oral: Interpretable Distribution Features with Maximum Testing Power »
Wittawat Jitkrittum · Zoltán Szabó · Kacper P Chwialkowski · Arthur Gretton -
2016 Poster: Interpretable Distribution Features with Maximum Testing Power »
Wittawat Jitkrittum · Zoltán Szabó · Kacper P Chwialkowski · Arthur Gretton -
2016 Poster: Statistical Inference for Cluster Trees »
Jisu KIM · Yen-Chi Chen · Sivaraman Balakrishnan · Alessandro Rinaldo · Larry Wasserman -
2016 Poster: Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences »
Chi Jin · Yuchen Zhang · Sivaraman Balakrishnan · Martin J Wainwright · Michael Jordan -
2016 Poster: Convergence guarantees for kernel-based quadrature rules in misspecified settings »
Motonobu Kanagawa · Bharath Sriperumbudur · Kenji Fukumizu -
2016 Poster: Mistake Bounds for Binary Matrix Completion »
Mark Herbster · Stephen Pasteris · Massimiliano Pontil -
2015 : The Benefit of Multitask Representation Learning »
Massimiliano Pontil -
2015 : *Arthur Gretton* Learning with Probabilities as Inputs, Using Kernels »
Arthur Gretton -
2015 Poster: Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families »
Heiko Strathmann · Dino Sejdinovic · Samuel Livingstone · Zoltan Szabo · Arthur Gretton -
2015 Poster: Optimal Rates for Random Fourier Features »
Bharath Sriperumbudur · Zoltan Szabo -
2015 Spotlight: Optimal Rates for Random Fourier Features »
Bharath Sriperumbudur · Zoltan Szabo -
2015 Poster: Fast Two-Sample Testing with Analytic Representations of Probability Measures »
Kacper P Chwialkowski · Aaditya Ramdas · Dino Sejdinovic · Arthur Gretton -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Poster: A Wild Bootstrap for Degenerate Kernel Tests »
Kacper P Chwialkowski · Dino Sejdinovic · Arthur Gretton -
2014 Oral: A Wild Bootstrap for Degenerate Kernel Tests »
Kacper P Chwialkowski · Dino Sejdinovic · Arthur Gretton -
2014 Poster: Kernel Mean Estimation via Spectral Filtering »
Krikamol Muandet · Bharath Sriperumbudur · Bernhard Schölkopf -
2014 Poster: Spectral k-Support Norm Regularization »
Andrew McDonald · Massimiliano Pontil · Dimitris Stamos -
2013 Workshop: New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks »
Urun Dogan · Marius Kloft · Tatiana Tommasi · Francesco Orabona · Massimiliano Pontil · Sinno Jialin Pan · Shai Ben-David · Arthur Gretton · Fei Sha · Marco Signoretto · Rajhans Samdani · Yun-Qian Miao · Mohammad Gheshlaghi azar · Ruth Urner · Christoph Lampert · Jonathan How -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Poster: B-test: A Non-parametric, Low Variance Kernel Two-sample Test »
Wojciech Zaremba · Arthur Gretton · Matthew B Blaschko -
2013 Poster: A Kernel Test for Three-Variable Interactions »
Dino Sejdinovic · Arthur Gretton · Wicher Bergsma -
2013 Oral: A Kernel Test for Three-Variable Interactions »
Dino Sejdinovic · Arthur Gretton · Wicher Bergsma -
2013 Poster: A New Convex Relaxation for Tensor Completion »
Bernardino Romera-Paredes · Massimiliano Pontil -
2013 Poster: Cluster Trees on Manifolds »
Sivaraman Balakrishnan · Srivatsan Narayanan · Alessandro Rinaldo · Aarti Singh · Larry Wasserman -
2012 Workshop: Confluence between Kernel Methods and Graphical Models »
Le Song · Arthur Gretton · Alexander Smola -
2012 Workshop: Algebraic Topology and Machine Learning »
Sivaraman Balakrishnan · Alessandro Rinaldo · Donald Sheehy · Aarti Singh · Larry Wasserman -
2012 Workshop: Modern Nonparametric Methods in Machine Learning »
Sivaraman Balakrishnan · Arthur Gretton · Mladen Kolar · John Lafferty · Han Liu · Tong Zhang -
2012 Poster: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Poster: Gradient-based kernel method for feature extraction and variable selection »
Kenji Fukumizu · Chenlei Leng -
2012 Spotlight: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2011 Poster: Kernel Bayes' Rule »
Kenji Fukumizu · Le Song · Arthur Gretton -
2011 Poster: Minimax Localization of Structural Information in Large Noisy Matrices »
Mladen Kolar · Sivaraman Balakrishnan · Alessandro Rinaldo · Aarti Singh -
2011 Poster: Noise Thresholds for Spectral Clustering »
Sivaraman Balakrishnan · Min Xu · Akshay Krishnamurthy · Aarti Singh -
2011 Spotlight: Noise Thresholds for Spectral Clustering »
Sivaraman Balakrishnan · Min Xu · Akshay Krishnamurthy · Aarti Singh -
2011 Spotlight: Minimax Localization of Structural Information in Large Noisy Matrices »
Mladen Kolar · Sivaraman Balakrishnan · Alessandro Rinaldo · Aarti Singh -
2011 Poster: Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint »
Bharath Sriperumbudur · Kenji Fukumizu · Gert Lanckriet -
2010 Workshop: Low-rank Methods for Large-scale Machine Learning »
Arthur Gretton · Michael W Mahoney · Mehryar Mohri · Ameet S Talwalkar -
2010 Spotlight: A Family of Penalty Functions for Structured Sparsity »
Charles A Micchelli · Jean M Morales · Massimiliano Pontil -
2010 Poster: A Family of Penalty Functions for Structured Sparsity »
Charles A Micchelli · Jean M Morales · Massimiliano Pontil -
2009 Workshop: Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing »
Stephane Canu · Olivier Cappé · Arthur Gretton · Zaid Harchaoui · Alain Rakotomamonjy · Jean-Philippe Vert -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
2009 Session: Oral session 10: Neural Modeling and Imaging »
Arthur Gretton -
2009 Poster: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Oral: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Poster: Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation »
Yusuke Watanabe · Kenji Fukumizu -
2009 Poster: On the Convergence of the Concave-Convex Procedure »
Bharath Sriperumbudur · Gert Lanckriet -
2009 Poster: Nonlinear directed acyclic structure learning with weakly additive noise models »
Robert E Tillman · Arthur Gretton · Peter Spirtes -
2009 Spotlight: Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation »
Yusuke Watanabe · Kenji Fukumizu -
2009 Poster: A Fast, Consistent Kernel Two-Sample Test »
Arthur Gretton · Kenji Fukumizu · Zaid Harchaoui · Bharath Sriperumbudur -
2009 Spotlight: A Fast, Consistent Kernel Two-Sample Test »
Arthur Gretton · Kenji Fukumizu · Zaid Harchaoui · Bharath Sriperumbudur -
2008 Workshop: Kernel Learning: Automatic Selection of Optimal Kernels »
Corinna Cortes · Arthur Gretton · Gert Lanckriet · Mehryar Mohri · Afshin Rostamizadeh -
2008 Poster: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Poster: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Spotlight: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Oral: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Poster: Fast Prediction on a Tree »
Mark Herbster · Massimiliano Pontil · Sergio Rojas Galeano -
2008 Oral: Fast Prediction on a Tree »
Mark Herbster · Massimiliano Pontil · Sergio Rojas Galeano -
2008 Session: Oral session 2: Sensorimotor Control »
Arthur Gretton -
2008 Poster: Learning Taxonomies by Dependence Maximization »
Matthew B Blaschko · Arthur Gretton -
2008 Poster: On-Line Prediction on Large Diameter Graphs »
Mark Herbster · Massimiliano Pontil · Guy Lever -
2007 Workshop: Representations and Inference on Probability Distributions »
Kenji Fukumizu · Arthur Gretton · Alexander Smola -
2007 Spotlight: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Spotlight: A Spectral Regularization Framework for Multi-Task Structure Learning »
Andreas Argyriou · Charles A. Micchelli · Massimiliano Pontil · Yiming Ying -
2007 Poster: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: A Spectral Regularization Framework for Multi-Task Structure Learning »
Andreas Argyriou · Charles A. Micchelli · Massimiliano Pontil · Yiming Ying -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Oral: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Kernels on Structured Objects Through Nested Histograms »
Marco Cuturi · Kenji Fukumizu -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Prediction on a Graph with a Perceptron »
Mark Herbster · Massimiliano Pontil -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Spotlight: Prediction on a Graph with a Perceptron »
Mark Herbster · Massimiliano Pontil -
2006 Poster: Multi-Task Feature Learning »
Andreas Argyriou · Theos Evgeniou · Massimiliano Pontil