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Transfer, domain adaptation and multi-task learning methods have been developed to better exploit the available data at training time, originally moved by the need to deal with a reduced amount of information. Nowadays, gathering data is much easier than in the past thanks to the low price of different acquisition devices (e.g. cameras) and to the World Wide Web that connects million of devices users. Existing methods must embrace new challenges to manage large scale data that do not lack anymore in size but may lack in quality or may continuously change over time. All this comes with several open questions, for instance:
- what are the limits of existing multi-task learning methods when the number of tasks grows while each task is described by only a small bunch of samples (“big T, small n”)?
- theory vs. practice: can multi-task learning for very big data (n>10^7) be performed with extremely randomized trees?
- what is the right way to leverage over noisy data gathered from the Internet as reference for a new task?
- can we get an advantage by overcoming the dataset bias and aligning multiple existing but partially related data collections before using them as source knowledge for a new target problem?
- how can an automatic system process a continuous stream of information in time and progressively adapt for life-long learning?
- since deep learning has demonstrated high performance on large scale data, is it possible to combine it with transfer and multiple kernel learning in a principled manner?
- can deep learning help to learn the right representation (e.g., task similarity matrix) in kernel-based transfer and 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?
- How can similarities across languages help us adapt to different domains in natural language processing tasks?
After the first workshop edition where we investigated new directions for learning across domains, we want now to call the attention of the machine learning community on the emerging problem of big data and its particular challenges regarding multi-task and transfer learning and its practical effects in many application areas like computer vision, robotics, medicine, bioinformatics etc. where transfer, domain adaptation and multi-task learning have been previously used with success. We will encourage applied researchers to contribute to the workshop in order to create a synergy with theoreticians and lead to a global advancement of the field.
A selection of the papers accepted to the workshop and voted by the reviewers will be re-evaluated also as invited contributions to the planned JMLR special topic on Domain Adaptation, Multi-task and Transfer Learning. The proposal for this special topic is currently under evaluation.
References:
[1] I. Kuzborskij, F. Orabona. Stability and Hypothesis Transfer Learning. ICML 2013
[2] T. Tommasi, F. Orabona, B. Caputo. Learning Categories from few Examples with Multi Model Knowledge Transfer. PAMI 36(5), 2014.
[3] U. Rückert, M. Kloft. Transfer Learning with Adaptive Regularizers. ECML 2011.
[4] A. Pentina, C. H. Lampert. A PAC-Bayesian bound for Lifelong Learning. ICML 2014.
[5] X. Glorot , A. Bordes , Y. Bengio. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach. ICML 2011.
[6] A. Kumar, A. Saha, H. Daumé III. A Co-regularization Based Semi-supervised Domain Adaptation. NIPS 2010.
[7] Cortes, Corinna, and Mehryar Mohri. Domain adaptation and sample bias correction theory and algorithm for regression. In Theoretical Computer Science 519 (2014): 103-126.
[8] C. Widmer, M. Kloft, G. Rätsch. Multi-task Learning for Computational Biology: Overview and Outlook. In Schölkopf et al: Festschrift in Honor of Vladimir Vapnik, Spinger 2013.
Author Information
Urun Dogan (Microsoft)
Tatiana Tommasi (KU Leuven)
Yoshua Bengio (Mila / U. Montreal)
Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.
Francesco Orabona (Stony Brook University)
Marius Kloft (TU Kaiserslautern)
Andres Munoz (Google)
Gunnar Rätsch (ETHZ)
Hal Daumé III (University of Maryland - College Park)
Hal Daumé III wields a professor appointment in Computer Science and Language Science at the University of Maryland, and spends time as a principal researcher in the machine learning group and fairness group at Microsoft Research in New York City. He and his wonderful advisees study questions related to how to get machines to become more adept at human language, by developing models and algorithms that allow them to learn from data. The two major questions that really drive their research these days are: (1) how can we get computers to learn language through natural interaction with people/users? and (2) how can we do this in a way that promotes fairness, transparency and explainability in the learned models?
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Xuezhi Wang (Google AI)
Daniel Hernández-lobato (Universidad Autonoma de Madrid)
Song Liu (Tokyo Institute of Technology)
Thomas Unterthiner (Google Research, Brain Team)
Pascal Germain (Université Laval)
Vinay P Namboodiri (University of Bath)
Michael Goetz (DKFZ Heidelberg)
Christopher Berlind (Georgia Institute of Technology)
Sigurd Spieckermann (Siemens)
Marta Soare (INRIA Lille - Nord Europe)
Yujia Li (University of Toronto)
Vitaly Kuznetsov (HRT)
Vitaly Kuznetsov is a research scientist at Google. Prior to joining Google Research, Vitaly received his Ph.D. in mathematics from the Courant Institute of Mathematical Sciences at New York University. Vitaly has contributed to a number of different areas in machine learning, in particular the development of the theory and algorithms for forecasting non-stationary time series. At Google, his work is focused on the design and implementation of large-scale machine learning tools and algorithms for time series modeling, forecasting and anomaly detection. His current research interests include all aspects of applied and theoretical time series analysis, in particular, in non-stationary environments.
Wenzhao Lian (Duke University)
Daniele Calandriello (DeepMind)
Emilie Morvant (LaHC, University of Saint-Etienne)
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2015 Workshop: Time Series Workshop »
Oren Anava · Azadeh Khaleghi · Vitaly Kuznetsov · Alexander Rakhlin -
2015 Poster: Rectified Factor Networks »
Djork-Arné Clevert · Andreas Mayr · Thomas Unterthiner · Sepp Hochreiter -
2015 Poster: Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms »
Yunwen Lei · Urun Dogan · Alexander Binder · Marius Kloft -
2015 Poster: Revenue Optimization against Strategic Buyers »
Mehryar Mohri · Andres Munoz -
2015 Poster: Learning Theory and Algorithms for Forecasting Non-stationary Time Series »
Vitaly Kuznetsov · Mehryar Mohri -
2015 Oral: Learning Theory and Algorithms for Forecasting Non-stationary Time Series »
Vitaly Kuznetsov · Mehryar Mohri -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
2014 Workshop: Representation and Learning Methods for Complex Outputs »
Richard Zemel · Dale Schuurmans · Kilian Q Weinberger · Yuhong Guo · Jia Deng · Francesco Dinuzzo · Hal Daumé III · Honglak Lee · Noah A Smith · Richard Sutton · Jiaqian YU · Vitaly Kuznetsov · Luke Vilnis · Hanchen Xiong · Calvin Murdock · Thomas Unterthiner · Jean-Francis Roy · Martin Renqiang Min · Hichem SAHBI · Fabio Massimo Zanzotto -
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 Workshop: Machine Learning for Clinical Data Analysis, Healthcare and Genomics »
Gunnar Rätsch · Madalina Fiterau · Julia Vogt -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Workshop: NIPS Workshop on Transactional Machine Learning and E-Commerce »
David Parkes · David H Wolpert · Jennifer Wortman Vaughan · Jacob D Abernethy · Amos Storkey · Mark Reid · Ping Jin · Nihar Bhadresh Shah · Mehryar Mohri · Luis E Ortiz · Robin Hanson · Aaron Roth · Satyen Kale · Sebastien Lahaie -
2014 Poster: Analysis of Brain States from Multi-Region LFP Time-Series »
Kyle R Ulrich · David Carlson · Wenzhao Lian · Jana S Borg · Kafui Dzirasa · Lawrence Carin -
2014 Poster: Flexible Transfer Learning under Support and Model Shift »
Xuezhi Wang · Jeff Schneider -
2014 Poster: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers »
Mehryar Mohri · Andres Munoz -
2014 Poster: Active Learning and Best-Response Dynamics »
Maria-Florina F Balcan · Christopher Berlind · Avrim Blum · Emma Cohen · Kaushik Patnaik · Le Song -
2014 Poster: Best-Arm Identification in Linear Bandits »
Marta Soare · Alessandro Lazaric · Remi Munos -
2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio -
2014 Poster: Learning to Search in Branch and Bound Algorithms »
He He · Hal Daumé III · Jason Eisner -
2014 Poster: Multi-Class Deep Boosting »
Vitaly Kuznetsov · Mehryar Mohri · Umar Syed -
2014 Poster: Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning »
Francesco Orabona -
2014 Spotlight: Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers »
Mehryar Mohri · Andres Munoz -
2014 Session: Oral Session 6 »
Mehryar Mohri -
2014 Poster: Conditional Swap Regret and Conditional Correlated Equilibrium »
Mehryar Mohri · Scott Yang -
2014 Poster: Generative Adversarial Nets »
Ian Goodfellow · Jean Pouget-Abadie · Mehdi Mirza · Bing Xu · David Warde-Farley · Sherjil Ozair · Aaron Courville · Yoshua Bengio -
2014 Poster: Mind the Nuisance: Gaussian Process Classification using Privileged Noise »
Daniel Hernández-lobato · Viktoriia Sharmanska · Kristian Kersting · Christoph Lampert · Novi Quadrianto -
2014 Poster: Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks »
Mario Marchand · Hongyu Su · Emilie Morvant · Juho Rousu · John Shawe-Taylor -
2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio -
2014 Demonstration: Neural Machine Translation »
Bart van Merriënboer · Kyunghyun Cho · Dzmitry Bahdanau · Yoshua Bengio -
2014 Oral: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Iterative Neural Autoregressive Distribution Estimator NADE-k »
Tapani Raiko · Yao Li · Kyunghyun Cho · Yoshua Bengio -
2014 Poster: Sparse Multi-Task Reinforcement Learning »
Daniele Calandriello · Alessandro Lazaric · Marcello Restelli -
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: 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: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Workshop: Output Representation Learning »
Yuhong Guo · Dale Schuurmans · Richard Zemel · Samy Bengio · Yoshua Bengio · Li Deng · Dan Roth · Kilian Q Weinberger · Jason Weston · Kihyuk Sohn · Florent Perronnin · Gabriel Synnaeve · Pablo R Strasser · julien audiffren · Carlo Ciliberto · Dan Goldwasser -
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: Learning Feature Selection Dependencies in Multi-task Learning »
Daniel Hernández-lobato · José Miguel Hernández-Lobato -
2013 Poster: Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent »
Yuening Hu · Jordan Boyd-Graber · Hal Daumé III · Z. Irene Ying -
2013 Poster: Dimension-Free Exponentiated Gradient »
Francesco Orabona -
2013 Spotlight: Dimension-Free Exponentiated Gradient »
Francesco Orabona -
2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato -
2013 Poster: Regression-tree Tuning in a Streaming Setting »
Samory Kpotufe · Francesco Orabona -
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 Poster: Multi-Prediction Deep Boltzmann Machines »
Ian Goodfellow · Mehdi Mirza · Aaron Courville · Yoshua Bengio -
2013 Poster: Generalized Denoising Auto-Encoders as Generative Models »
Yoshua Bengio · Li Yao · Guillaume Alain · Pascal Vincent -
2013 Poster: Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs »
Yann Dauphin · Yoshua Bengio -
2012 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · James Bergstra · Quoc V. Le -
2012 Poster: Accuracy at the Top »
Stephen Boyd · Corinna Cortes · Mehryar Mohri · Ana Radovanovic -
2012 Poster: On Multilabel Classification and Ranking with Partial Feedback »
Claudio Gentile · Francesco Orabona -
2012 Poster: Imitation Learning by Coaching »
He He · Hal Daumé III · Jason Eisner -
2012 Poster: Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression »
Piyush Rai · Abhishek Kumar · Hal Daumé III -
2012 Spotlight: On Multilabel Classification and Ranking with Partial Feedback »
Claudio Gentile · Francesco Orabona -
2012 Poster: Spectral Learning of General Weighted Automata via Constrained Matrix Completion »
Borja Balle · Mehryar Mohri -
2012 Session: Oral Session 4 »
Gunnar Rätsch -
2012 Oral: Spectral Learning of General Weighted Automata via Constrained Matrix Completion »
Borja Balle · Mehryar Mohri -
2012 Poster: Density-Difference Estimation »
Masashi Sugiyama · Takafumi Kanamori · Taiji Suzuki · Marthinus C du Plessis · Song Liu · Ichiro Takeuchi -
2012 Poster: Learned Prioritization for Trading Off Accuracy and Speed »
Jiarong Jiang · Adam Teichert · Hal Daumé III · Jason Eisner -
2011 Workshop: Machine Learning in Computational Biology »
Jean-Philippe Vert · Gunnar Rätsch · Yanjun Qi · Tomer Hertz · Anna Goldenberg · Christina Leslie -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · Adam Coates · Yann LeCun · Nicolas Le Roux · Andrew Y Ng -
2011 Workshop: Sparse Representation and Low-rank Approximation »
Ameet S Talwalkar · Lester W Mackey · Mehryar Mohri · Michael W Mahoney · Francis Bach · Mike Davies · Remi Gribonval · Guillaume R Obozinski -
2011 Oral: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Shallow vs. Deep Sum-Product Networks »
Olivier Delalleau · Yoshua Bengio -
2011 Poster: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Message-Passing for Approximate MAP Inference with Latent Variables »
Jiarong Jiang · Piyush Rai · Hal Daumé III -
2011 Poster: The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning »
Marius Kloft · Gilles Blanchard -
2011 Poster: Algorithms for Hyper-Parameter Optimization »
James Bergstra · Rémi Bardenet · Yoshua Bengio · Balázs Kégl -
2011 Poster: Co-regularized Multi-view Spectral Clustering »
Abhishek Kumar · Piyush Rai · Hal Daumé III -
2011 Poster: Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation »
Nico Goernitz · Christian Widmer · Georg Zeller · Andre Kahles · Soeren Sonnenburg · Gunnar Rätsch -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2011 Poster: Robust Multi-Class Gaussian Process Classification »
Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont -
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: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2010 Workshop: Machine Learning in Computational Biology »
Gunnar Rätsch · Jean-Philippe Vert · Tomer Hertz · Yanjun Qi -
2010 Poster: New Adaptive Algorithms for Online Classification »
Francesco Orabona · Yacov Crammer -
2010 Poster: Learning Multiple Tasks using Manifold Regularization »
Arvind Agarwal · Hal Daumé III · Samuel Gerber -
2010 Poster: Learning Bounds for Importance Weighting »
Corinna Cortes · Yishay Mansour · Mehryar Mohri -
2010 Spotlight: Learning from Candidate Labeling Sets »
Jie Luo · Francesco Orabona -
2010 Poster: Learning from Candidate Labeling Sets »
Jie Luo · Francesco Orabona -
2010 Poster: Co-regularization Based Semi-supervised Domain Adaptation »
Hal Daumé III · Abhishek Kumar · Avishek Saha -
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 Poster: Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models »
Gideon S Mann · Ryan McDonald · Mehryar Mohri · Nathan Silberman · Dan Walker -
2009 Poster: Ensemble Nystrom Method »
Sanjiv Kumar · Mehryar Mohri · Ameet S Talwalkar -
2009 Poster: From PAC-Bayes Bounds to KL Regularization »
Pascal Germain · Alexandre Lacasse · Francois Laviolette · Mario Marchand · Sara Shanian -
2009 Poster: Slow, Decorrelated Features for Pretraining Complex Cell-like Networks »
James Bergstra · Yoshua Bengio -
2009 Poster: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Poster: Multi-Label Prediction via Sparse Infinite CCA »
Piyush Rai · Hal Daumé III -
2009 Spotlight: Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models »
Gideon S Mann · Ryan McDonald · Mehryar Mohri · Nathan Silberman · Dan Walker -
2009 Poster: Learning Non-Linear Combinations of Kernels »
Corinna Cortes · Mehryar Mohri · Afshin Rostamizadeh -
2009 Poster: Efficient and Accurate Lp-Norm Multiple Kernel Learning »
Marius Kloft · Ulf Brefeld · Soeren Sonnenburg · Pavel Laskov · Klaus-Robert Müller · Alexander Zien -
2009 Session: Debate on Future Publication Models for the NIPS Community »
Yoshua Bengio -
2009 Poster: Polynomial Semantic Indexing »
Bing Bai · Jason E Weston · David Grangier · Ronan Collobert · Kunihiko Sadamasa · Yanjun Qi · Corinna Cortes · Mehryar Mohri -
2008 Workshop: Kernel Learning: Automatic Selection of Optimal Kernels »
Corinna Cortes · Arthur Gretton · Gert Lanckriet · Mehryar Mohri · Afshin Rostamizadeh -
2008 Workshop: Machine Learning in Computational Biology »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch -
2008 Mini Symposium: Machine Learning in Computational Biology »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch -
2008 Poster: Domain Adaptation with Multiple Sources »
Yishay Mansour · Mehryar Mohri · Afshin Rostamizadeh -
2008 Spotlight: Domain Adaptation with Multiple Sources »
Yishay Mansour · Mehryar Mohri · Afshin Rostamizadeh -
2008 Poster: An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis »
Gabriele B Schweikert · Christian Widmer · Bernhard Schölkopf · Gunnar Rätsch -
2008 Poster: Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression »
Piyush Rai · Hal Daumé III -
2008 Poster: Rademacher Complexity Bounds for Non-I.I.D. Processes »
Mehryar Mohri · Afshin Rostamizadeh -
2007 Workshop: Machine Learning in Computational Biology (Part 2) »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch · Koji Tsuda -
2007 Workshop: Machine Learning in Computational Biology (Part 1) »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch · Koji Tsuda -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Poster: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Poster: Learning the 2-D Topology of Images »
Nicolas Le Roux · Yoshua Bengio · Pascal Lamblin · Marc Joliveau · Balázs Kégl -
2007 Spotlight: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Spotlight: Boosting Algorithms for Maximizing the Soft Margin »
Manfred K. Warmuth · Karen Glocer · Gunnar Rätsch -
2007 Poster: Boosting Algorithms for Maximizing the Soft Margin »
Manfred K. Warmuth · Karen Glocer · Gunnar Rätsch -
2007 Poster: Topmoumoute Online Natural Gradient Algorithm »
Nicolas Le Roux · Pierre-Antoine Manzagol · Yoshua Bengio -
2007 Poster: Stability Bounds for Non-i.i.d. Processes »
Mehryar Mohri · Afshin Rostamizadeh -
2006 Workshop: New Problems and Methods in Computational Biology »
Gal Chechik · Quaid Morris · Koji Tsuda · Gunnar Rätsch · Christina Leslie · William S Noble -
2006 Poster: Large Scale Hidden Semi-Markov SVMs »
Gunnar Rätsch · Soeren Sonnenburg -
2006 Poster: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Talk: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Poster: On Transductive Regression »
Corinna Cortes · Mehryar Mohri -
2006 Demonstration: SHOGUN Machine Learning Toolbox »
Soeren Sonnenburg · Gunnar Rätsch