Timezone: »
The main objective of the workshop is to document and discuss the recent rise of new research questions on the general problem of learning across domains and tasks. This includes the main topics of transfer [1,2,3] and multi-task learning [4], together with several related variants as domain adaptation [5,6] and dataset bias [7].
In the last years there has been an increasing boost of activity in these areas, many of them driven by practical applications, such as object categorization. Different solutions were studied for the considered topics, mainly separately and without a joint theoretical framework. On the other hand, most of the existing theoretical formulations model regimes that are rarely used in practice (e.g. adaptive methods that store all the source samples).
The workshop will focus on closing this gap by providing an opportunity for theoreticians and practitioners to get together in one place, to share and debate over current theories and empirical results. The goal is to promote a fruitful exchange of ideas and methods between the different communities, leading to a global advancement of the field.
Transfer Learning - Transfer Learning (TL) refers to the problem of retaining and applying the knowledge available for one or more source tasks, to efficiently develop an hypothesis for a new target task. Each task may contain the same (domain adaptation) or different label sets (across category transfer). Most of the effort has been devoted to binary classification, while most interesting practical transfer problems are intrinsically multi-class and the number of classes can often increase in time. Hence, it is natural to ask:
- How to formalize knowledge transfer across multi-class tasks and provide theoretical guarantees on this setting?
- Moreover, can interclass transfer and incremental class learning be properly integrated?
- Can learning guarantees be provided when the adaptation relies only on pre-trained source hypotheses without explicit access to the source samples, as it is often the case in real world scenarios?
Multi-task Learning - Learning over multiple related tasks can outperform learning each task in isolation. This is the principal assertion of Multi-task learning (MTL) and implies that the learning process may benefit from common information shared across the tasks. In the simplest case, transfer process is symmetric and all the tasks are considered as equally related and appropriate for joint training.
- What happens when this condition does not hold, e.g., how to avoid negative transfer?
- Moreover, can RHKS embeddings be adequately integrated into the learning process to estimate and compare the distributions underlying the multiple tasks?
- How may embedding probability distributions help learning from data clouds?
- Recent methods, like deep learning or multiple kernel learning, can help to get a step closer towards the complete automatization of multi-task learning?
- How can notions from reinforcement learning such as source task selection be connected to notions from convex multi-task learning such as the task similarity matrix?
References
[1] I. Kuzborskij and F. Orabona. Stability and Hypothesis Transfer Learning. ICML 2013
[2] T. Tommasi, F. Orabona, B. Caputo. Safety in Numbers: Learning Categories from Few Examples with Multi Model Knowledge Transfer. CVPR 2010.
[3] U. Rückert, M. Kloft. Transfer Learning with Adaptive Regularizers. ECML 2011.
[4] A. Maurer, M. Pontil, B. Romera-Paredes. Sparse coding for multitask and transfer learning. ICML 2013.
[5] S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, J. Wortman Vaughan. A theory of learning from different domains. Machine Learning 2010.
[6] K. Saenko, B. Kulis, M. Fritz, T. Darrell. Adapting Visual Category Models to New Domains. ECCV 2010.
[7] A. Torralba, A. Efros. Unbiased Look at Dataset Bias. CVPR 2011.
Author Information
Urun Dogan (Microsoft)
Marius Kloft (TU Kaiserslautern)
Tatiana Tommasi (KUL)
Francesco Orabona (Stony Brook University)
Massimiliano Pontil (IIT & UCL)
Sinno Jialin Pan (The Chinese University of Hong Kong)
Shai Ben-David (University of Waterloo)
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).
Fei Sha (University of Southern California (USC))
Marco Signoretto (KULeuven)
Rajhans Samdani (Google Inc.)
Yun-Qian Miao (University of Waterloo)
Mohammad Gheshlaghi azar (CMU)
Ruth Urner (York University)
Christoph Lampert (Institute of Science and Technology Austria (ISTA))

Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. In 2010 he joined the Institute of Science and Technology Austria (ISTA) first as an Assistant Professor and since 2015 as a Professor. There, he leads the research group for Machine Learning and Computer Vision, and since 2019 he is also the head of ISTA's ELLIS unit.
Jonathan How (MIT)
More from the Same Authors
-
2021 : SSSE: Efficiently Erasing Samples from Trained Machine Learning Models »
Alexandra Peste · Dan Alistarh · Christoph Lampert -
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 -
2021 : Hierarchical Topic Evaluation: Statistical vs. Neural Models »
Mayank Kumar Nagda · Charu Karakkaparambil James · Sophie Burkhardt · Marius Kloft -
2021 : Poster: On the Impossibility of Fairness-Aware Learning from Corrupted Data »
Nikola Konstantinov · Christoph Lampert -
2023 Poster: Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model »
Peter Súkeník · Marco Mondelli · Christoph Lampert -
2023 Poster: Labeling Neural Representations with Inverse Recognition »
Kirill Bykov · Laura Kopf · Shinichi Nakajima · Marius Kloft · Marina Höhne -
2023 Poster: Zero-Shot Batch-Level Anomaly Detection »
Aodong Li · Chen Qiu · Marius Kloft · Padhraic Smyth · Maja Rudolph · Stephan Mandt -
2023 Poster: Nonlinear Meta-Learning Can Guarantee Faster Rates »
Dimitri Meunier · Zhu Li · Arthur Gretton · Samory Kpotufe -
2023 Poster: MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting »
Felix Biggs · Antonin Schrab · Arthur Gretton -
2023 Poster: Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start »
Riccardo Grazzi · Massimiliano Pontil · Saverio Salzo -
2023 Poster: MMD Aggregated Two-Sample Test »
Antonin Schrab · Ilmun Kim · Mélisande Albert · Béatrice Laurent · Benjamin Guedj · Arthur Gretton -
2022 : Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings. »
Ajay Chawda · Marius Kloft · Stefanie Grimm -
2022 Poster: Optimal Rates for Regularized Conditional Mean Embedding Learning »
Zhu Li · Dimitri Meunier · Mattes Mollenhauer · Arthur Gretton -
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: 3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds »
Antonio Alliegro · Francesco Cappio Borlino · Tatiana Tommasi -
2022 Poster: Robustness to Unbounded Smoothness of Generalized SignSGD »
Michael Crawshaw · Mingrui Liu · Francesco Orabona · Wei Zhang · Zhenxun Zhuang -
2022 Poster: Fairness-Aware PAC Learning from Corrupted Data »
Nikola Konstantinov · Christoph Lampert -
2022 Poster: Influencing Long-Term Behavior in Multiagent Reinforcement Learning »
Dong-Ki Kim · Matthew Riemer · Miao Liu · Jakob Foerster · Michael Everett · Chuangchuang Sun · Gerald Tesauro · Jonathan How -
2021 Workshop: Machine Learning Meets Econometrics (MLECON) »
David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou -
2021 : On the Impossibility of Fairness-Aware Learning from Corrupted Data »
Nikola Konstantinov · Christoph Lampert -
2021 Poster: KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support »
Pierre Glaser · Michael Arbel · Arthur Gretton -
2021 Poster: Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers »
Jeffrey Negrea · Blair Bilodeau · Nicolò Campolongo · Francesco Orabona · Dan Roy -
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: Fine-grained Generalization Analysis of Inductive Matrix Completion »
Antoine Ledent · Rodrigo Alves · Yunwen Lei · Marius Kloft -
2020 Poster: Sharper Generalization Bounds for Pairwise Learning »
Yunwen Lei · Antoine Ledent · Marius Kloft -
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: Estimating weighted areas under the ROC curve »
Andreas Maurer · Massimiliano Pontil -
2020 Poster: A kernel test for quasi-independence »
Tamara Fernandez · Wenkai Xu · Marc Ditzhaus · Arthur Gretton -
2020 Poster: Temporal Variability in Implicit Online Learning »
Nicolò Campolongo · Francesco Orabona -
2020 Spotlight: A kernel test for quasi-independence »
Tamara Fernandez · Wenkai Xu · Marc Ditzhaus · Arthur Gretton -
2020 Poster: Unsupervised object-centric video generation and decomposition in 3D »
Paul Henderson · Christoph Lampert -
2020 Poster: Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning »
Jianda Chen · Shangyu Chen · Sinno Jialin Pan -
2020 Session: Orals & Spotlights Track 01: Representation/Relational »
Laurens van der Maaten · Fei Sha -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 Poster: Momentum-Based Variance Reduction in Non-Convex SGD »
Ashok Cutkosky · Francesco Orabona -
2019 Poster: Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration »
Kwang-Sung Jun · Ashok Cutkosky · Francesco Orabona -
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: Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network »
Siqi Wang · Yijie Zeng · Xinwang Liu · En Zhu · Jianping Yin · Chuanfu Xu · Marius Kloft -
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: MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization »
Shangyu Chen · Wenya Wang · Sinno Jialin Pan -
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: BRUNO: A Deep Recurrent Model for Exchangeable Data »
Iryna Korshunova · Jonas Degrave · Ferenc Huszar · Yarin Gal · Arthur Gretton · Joni Dambre -
2018 Poster: Synthesize Policies for Transfer and Adaptation across Tasks and Environments »
Hexiang Hu · Liyu Chen · Boqing Gong · Fei Sha -
2018 Spotlight: Synthesize Policies for Transfer and Adaptation across Tasks and Environments »
Hexiang Hu · Liyu Chen · Boqing Gong · Fei Sha -
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: Optimal Transport and Machine Learning »
Olivier Bousquet · Marco Cuturi · Gabriel Peyré · Fei Sha · Justin Solomon -
2017 Workshop: Workshop on Prioritising Online Content »
John Shawe-Taylor · Massimiliano Pontil · Nicolò Cesa-Bianchi · Emine Yilmaz · Chris Watkins · Sebastian Riedel · Marko Grobelnik -
2017 Workshop: Learning with Limited Labeled Data: Weak Supervision and Beyond »
Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré -
2017 : Marius Kloft (Kaiserslautern) on Generalization Error Bounds for Extreme Multi-class Classification »
Marius Kloft -
2017 : Conditional Densities and Efficient Models in Infinite Exponential Families »
Arthur Gretton -
2017 Workshop: Extreme Classification: Multi-class & Multi-label Learning in Extremely Large Label Spaces »
Manik Varma · Marius Kloft · Krzysztof Dembczynski -
2017 Poster: Multi-Task Learning for Contextual Bandits »
Aniket Anand Deshmukh · Urun Dogan · Clay Scott -
2017 Poster: A Linear-Time Kernel Goodness-of-Fit Test »
Wittawat Jitkrittum · Wenkai Xu · Zoltan Szabo · Kenji Fukumizu · Arthur Gretton -
2017 Poster: Training Deep Networks without Learning Rates Through Coin Betting »
Francesco Orabona · Tatiana Tommasi -
2017 Oral: A Linear-Time Kernel Goodness-of-Fit Test »
Wittawat Jitkrittum · Wenkai Xu · Zoltan Szabo · Kenji Fukumizu · Arthur Gretton -
2017 Poster: Consistent Multitask Learning with Nonlinear Output Relations »
Carlo Ciliberto · Alessandro Rudi · Lorenzo Rosasco · Massimiliano Pontil -
2017 Poster: An Empirical Study on The Properties of Random Bases for Kernel Methods »
Maximilian Alber · Pieter-Jan Kindermans · Kristof Schütt · Klaus-Robert Müller · Fei Sha -
2017 Poster: Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon »
Xin Dong · Shangyu Chen · Sinno Pan -
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: Clustering with Same-Cluster Queries »
Hassan Ashtiani · Shrinu Kushagra · Shai Ben-David -
2016 Oral: Clustering with Same-Cluster Queries »
Hassan Ashtiani · Shrinu Kushagra · Shai Ben-David -
2016 Poster: Improving PAC Exploration Using the Median Of Means »
Jason Pazis · Ronald Parr · Jonathan How -
2016 Poster: Coin Betting and Parameter-Free Online Learning »
Francesco Orabona · David Pal -
2016 Poster: Mistake Bounds for Binary Matrix Completion »
Mark Herbster · Stephen Pasteris · Massimiliano Pontil -
2015 : Domain Adaptation for Binary Classification »
Shai Ben-David -
2015 : The Benefit of Multitask Representation Learning »
Massimiliano Pontil -
2015 : On Weight Ratio Estimation for Covariate Shift »
Ruth Urner -
2015 Workshop: Transfer and Multi-Task Learning: Trends and New Perspectives »
Anastasia Pentina · Christoph Lampert · Sinno Jialin Pan · Mingsheng Long · Judy Hoffman · Baochen Sun · Kate Saenko -
2015 : Discussion Panel »
Tim van Erven · Wouter Koolen · Peter Grünwald · Shai Ben-David · Dylan Foster · Satyen Kale · Gergely Neu -
2015 : Do Shallow Kernel Methods Match Deep Neural Networks »
Fei Sha -
2015 : *Arthur Gretton* Learning with Probabilities as Inputs, Using Kernels »
Arthur Gretton -
2015 : Clustering Is Easy When... »
Shai Ben-David -
2015 : Do Shallow Kernel Methods Match Deep Neural Networks? »
Fei Sha -
2015 Poster: Lifelong Learning with Non-i.i.d. Tasks »
Anastasia Pentina · Christoph Lampert -
2015 Poster: Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms »
Yunwen Lei · Urun Dogan · Alexander Binder · Marius Kloft -
2015 Poster: Streaming, Distributed Variational Inference for Bayesian Nonparametrics »
Trevor Campbell · Julian Straub · John Fisher III · Jonathan How -
2015 Poster: Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families »
Heiko Strathmann · Dino Sejdinovic · Samuel Livingstone · Zoltan Szabo · Arthur Gretton -
2015 Poster: Fast Two-Sample Testing with Analytic Representations of Probability Measures »
Kacper P Chwialkowski · Aaditya Ramdas · Dino Sejdinovic · Arthur Gretton -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
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 Poster: Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning »
Francesco Orabona -
2014 Oral: A Wild Bootstrap for Degenerate Kernel Tests »
Kacper P Chwialkowski · Dino Sejdinovic · Arthur Gretton -
2014 Poster: Diverse Sequential Subset Selection for Supervised Video Summarization »
Boqing Gong · Wei-Lun Chao · Kristen Grauman · Fei Sha -
2014 Poster: Mind the Nuisance: Gaussian Process Classification using Privileged Noise »
Daniel Hernández-lobato · Viktoriia Sharmanska · Kristian Kersting · Christoph Lampert · Novi Quadrianto -
2014 Poster: Spectral k-Support Norm Regularization »
Andrew McDonald · Massimiliano Pontil · Dimitris Stamos -
2013 Workshop: MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2) »
Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen -
2013 Workshop: Advances in Machine Learning for Sensorimotor Control »
Thomas Walsh · Alborz Geramifard · Marc Deisenroth · Jonathan How · Jan Peters -
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 Workshop: MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 1) »
Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen -
2013 Poster: Reshaping Visual Datasets for Domain Adaptation »
Boqing Gong · Kristen Grauman · Fei Sha -
2013 Poster: Dimension-Free Exponentiated Gradient »
Francesco Orabona -
2013 Spotlight: Dimension-Free Exponentiated Gradient »
Francesco Orabona -
2013 Poster: Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture »
Trevor Campbell · Miao Liu · Brian Kulis · Jonathan How · Lawrence Carin -
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 Poster: Regression-tree Tuning in a Streaming Setting »
Samory Kpotufe · Francesco Orabona -
2013 Poster: Sequential Transfer in Multi-armed Bandit with Finite Set of Models »
Mohammad Gheshlaghi azar · Alessandro Lazaric · Emma Brunskill -
2013 Poster: Learning Kernels Using Local Rademacher Complexity »
Corinna Cortes · Marius Kloft · Mehryar Mohri -
2013 Spotlight: Regression-tree Tuning in a Streaming Setting »
Samory Kpotufe · Francesco Orabona -
2013 Spotlight: Learning Kernels Using Local Rademacher Complexity »
Corinna Cortes · Marius Kloft · Mehryar Mohri -
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: Similarity Component Analysis »
Soravit Changpinyo · Kuan Liu · Fei Sha -
2013 Poster: Sensor Selection in High-Dimensional Gaussian Trees with Nuisances »
Daniel S Levine · Jonathan How -
2012 Workshop: Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty »
Jonathan How · Lawrence Carin · John Fisher III · Michael Jordan · Alborz Geramifard -
2012 Workshop: Confluence between Kernel Methods and Graphical Models »
Le Song · Arthur Gretton · Alexander Smola -
2012 Workshop: Modern Nonparametric Methods in Machine Learning »
Sivaraman Balakrishnan · Arthur Gretton · Mladen Kolar · John Lafferty · Han Liu · Tong Zhang -
2012 Poster: On Multilabel Classification and Ranking with Partial Feedback »
Claudio Gentile · Francesco Orabona -
2012 Poster: Non-linear Metric Learning »
Dor Kedem · Stephen Tyree · Kilian Q Weinberger · Fei Sha · Gert Lanckriet -
2012 Poster: Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction »
Christoph Lampert -
2012 Spotlight: On Multilabel Classification and Ranking with Partial Feedback »
Claudio Gentile · Francesco Orabona -
2012 Session: Oral Session 5 »
Fei Sha -
2012 Poster: Semantic Kernel Forests from Multiple Taxonomies »
Sung Ju Hwang · Kristen Grauman · Fei Sha -
2012 Poster: Optimal kernel choice for large-scale two-sample tests »
Arthur Gretton · Bharath Sriperumbudur · Dino Sejdinovic · Heiko Strathmann · Sivaraman Balakrishnan · Massimiliano Pontil · Kenji Fukumizu -
2011 Poster: Kernel Bayes' Rule »
Kenji Fukumizu · Le Song · Arthur Gretton -
2011 Poster: Learning a Tree of Metrics with Disjoint Visual Features »
Sung Ju Hwang · Kristen Grauman · Fei Sha -
2011 Poster: Maximum Margin Multi-Label Structured Prediction »
Christoph Lampert -
2011 Poster: The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning »
Marius Kloft · Gilles Blanchard -
2010 Workshop: Low-rank Methods for Large-scale Machine Learning »
Arthur Gretton · Michael W Mahoney · Mehryar Mohri · Ameet S Talwalkar -
2010 Workshop: New Directions in Multiple Kernel Learning »
Marius Kloft · Ulrich Rueckert · Cheng Soon Ong · Alain Rakotomamonjy · Soeren Sonnenburg · Francis Bach -
2010 Workshop: Challenges of Data Visualization »
Barbara Hammer · Laurens van der Maaten · Fei Sha · Alexander Smola -
2010 Workshop: Tensors, Kernels, and Machine Learning »
Tamara G Kolda · Vicente Malave · David F Gleich · Johan Suykens · Marco Signoretto · Andreas Argyriou -
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 -
2010 Poster: New Adaptive Algorithms for Online Classification »
Francesco Orabona · Yacov Crammer -
2010 Poster: Unsupervised Kernel Dimension Reduction »
Meihong Wang · Fei Sha · Michael Jordan -
2010 Spotlight: Learning from Candidate Labeling Sets »
Jie Luo · Francesco Orabona -
2010 Poster: Learning from Candidate Labeling Sets »
Jie Luo · Francesco Orabona -
2010 Poster: Towards Property-Based Classification of Clustering Paradigms »
Margareta Ackerman · Shai Ben-David · David R Loker -
2009 Workshop: Learning from Multiple Sources with Applications to Robotics »
Barbara Caputo · Nicolò Cesa-Bianchi · David R Hardoon · Gayle Leen · Francesco Orabona · Jaakko Peltonen · Simon Rogers -
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: Transfer Learning for Structured Data »
Sinno Jialin Pan · Ivor W Tsang · Le Song · Karsten Borgwardt · Qiang Yang -
2009 Workshop: Clustering: Science or art? Towards principled approaches »
Margareta Ackerman · Shai Ben-David · Avrim Blum · Isabelle Guyon · Ulrike von Luxburg · Robert Williamson · Reza Zadeh -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
2009 Workshop: Statistical Machine Learning for Visual Analytics »
Guy Lebanon · Fei Sha -
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: Efficient and Accurate Lp-Norm Multiple Kernel Learning »
Marius Kloft · Ulf Brefeld · Soeren Sonnenburg · Pavel Laskov · Klaus-Robert Müller · Alexander Zien -
2009 Poster: Nonlinear directed acyclic structure learning with weakly additive noise models »
Robert E Tillman · Arthur Gretton · Peter Spirtes -
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 Workshop: New Challanges in Theoretical Machine Learning: Data Dependent Concept Spaces »
Maria-Florina F Balcan · Shai Ben-David · Avrim Blum · Kristiaan Pelckmans · John Shawe-Taylor -
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 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
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: Measures of Clustering Quality: A Working Set of Axioms for Clustering »
Shai Ben-David · Margareta Ackerman -
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 -
2008 Oral: Measures of Clustering Quality: A Working Set of Axioms for Clustering »
Shai Ben-David · Margareta Ackerman -
2008 Session: Oral session 1: Clustering »
Fei Sha -
2007 Workshop: Representations and Inference on Probability Distributions »
Kenji Fukumizu · Arthur Gretton · Alexander Smola -
2007 Workshop: Machine Learning for Systems Problems (Part 2) »
Archana Ganapathi · Sumit Basu · Fei Sha · Emre Kiciman -
2007 Workshop: Machine Learning for Systems Problems (Part 1) »
Archana Ganapathi · Sumit Basu · Fei Sha · Emre Kiciman -
2007 Session: Session 7: Systems and Applications »
Fei Sha -
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: Large Margin Gaussian Mixture Models for Automatic Speech Recognition »
Fei Sha · Lawrence Saul -
2006 Talk: Large Margin Gaussian Mixture Models for Automatic Speech Recognition »
Fei Sha · Lawrence Saul -
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: Analysis of Representations for Domain Adaptation »
John Blitzer · Shai Ben-David · Yacov Crammer · Fernando Pereira -
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 -
2006 Poster: Graph Regularization for Maximum Variance Unfolding with an Application to Sensor Localization »
Kilian Q Weinberger · Fei Sha · Qihui Zhu · Lawrence Saul