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Learning problems that involve complex outputs are becoming increasingly prevalent in machine learning research. For example, work on image and document tagging now considers thousands of labels chosen from an open vocabulary, with only partially labeled instances available for training. Given limited labeled data, these settings also create zero-shot learning problems with respect to omitted tags, leading to the challenge of inducing semantic label representations. Furthermore, prediction targets are often abstractions that are difficult to predict from raw input data, but can be better predicted from learned latent representations. Finally, when labels exhibit complex inter-relationships it is imperative to capture latent label relatedness to improve generalization.
This workshop will bring together separate communities that have been working on novel representation and learning methods for problems with complex outputs. Although representation learning has already achieved state of the art results in standard settings, recent research has begun to explore the use of learned representations in more complex scenarios, such as structured output prediction, multiple modality co-embedding, multi-label prediction, and zero shot learning. Unfortunately, these emerging research topics have been conducted in separate sub-areas, without proper connections drawn to similar ideas in other areas, hence general methods and understanding have not yet emerged from the disconnected pursuits. The aim of this workshop is to identify fundamental strategies, highlight differences, and identify the prospects for developing a set of systematic theory and methods for learning problems with complex outputs. The target communities include researchers working on image tagging, document categorization, natural language processing, large vocabulary speech recognition, deep learning, latent variable modeling, and large scale multi-label learning.
Relevant topics include:
- Multi-label learning with large and/or incomplete output spaces
- Zero-shot learning
- Label embedding and Co-embedding
- Learning output kernels
- Output structure learning
Author Information
Richard Zemel (Vector Institute/University of Toronto)
Dale Schuurmans (University of Alberta & Google Brain)
Kilian Q Weinberger (Washington University in St. Louis)
Yuhong Guo (Carleton University)
Jia Deng (University of Michigan)
Francesco Dinuzzo (Expedia Group)
Hal Daumé III (University of Maryland - College Park)
Honglak Lee (LG AI Research / U. Michigan)
Noah A Smith (Carnegie Mellon University)
Richard Sutton (DeepMind, U Alberta)
Richard S. Sutton is a professor and iCORE chair in the department of computing science at the University of Alberta. He is a fellow of the Association for the Advancement of Artificial Intelligence and co-author of the textbook "Reinforcement Learning: An Introduction" from MIT Press. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.
Jiaqian YU (Ecole Centrale Paris)
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.
Luke Vilnis (University of Massachusetts, Amherst)
Hanchen Xiong (University of Innsbruck)
Calvin Murdock (Carnegie Mellon University)
Thomas Unterthiner (Google Research, Brain Team)
Jean-Francis Roy (Université Laval)
Martin Renqiang Min (NEC Labs America)
Hichem SAHBI (CNRS, TELECOM ParisTech)
Fabio Massimo Zanzotto (University of Rome "Tor Vergata")
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2012 Poster: Learned Prioritization for Trading Off Accuracy and Speed »
Jiarong Jiang · Adam Teichert · Hal Daumé III · Jason Eisner -
2012 Poster: A Polynomial-time Form of Robust Regression »
Yao-Liang Yu · Özlem Aslan · Dale Schuurmans -
2012 Poster: Cardinality Restricted Boltzmann Machines »
Kevin Swersky · Danny Tarlow · Ilya Sutskever · Richard Zemel · Russ Salakhutdinov · Ryan Adams -
2011 Workshop: Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity »
Greg Shakhnarovich · Dhruv Batra · Brian Kulis · Kilian Q Weinberger -
2011 Poster: Message-Passing for Approximate MAP Inference with Latent Variables »
Jiarong Jiang · Piyush Rai · Hal Daumé III -
2011 Invited Talk: Learning About Sensorimotor Data »
Richard Sutton -
2011 Poster: Co-regularized Multi-view Spectral Clustering »
Abhishek Kumar · Piyush Rai · Hal Daumé III -
2011 Poster: Co-Training for Domain Adaptation »
Minmin Chen · Kilian Q Weinberger · John Blitzer -
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2010 Session: Oral Session 16 »
Kilian Q Weinberger -
2010 Poster: Active Instance Sampling via Matrix Partition »
Yuhong Guo -
2010 Poster: Learning Multiple Tasks using Manifold Regularization »
Arvind Agarwal · Hal Daumé III · Samuel Gerber -
2010 Poster: Large Margin Multi-Task Metric Learning »
Shibin Parameswaran · Kilian Q Weinberger -
2010 Poster: Relaxed Clipping: A Global Training Method for Robust Regression and Classification »
Yao-Liang Yu · Min Yang · Linli Xu · Martha White · Dale Schuurmans -
2010 Talk: Opening Remarks and Awards »
Richard Zemel · Terrence Sejnowski · John Shawe-Taylor -
2010 Poster: Decoding Ipsilateral Finger Movements from ECoG Signals in Humans »
Yuzong Liu · Mohit Sharma · Charles M Gaona · Jonathan D Breshears · jarod Roland · zachary V Freudenburg · Kilian Q Weinberger · Eric C Leuthardt -
2010 Poster: Empirical Risk Minimization with Approximations of Probabilistic Grammars »
Shay Cohen · Noah A Smith -
2010 Poster: Co-regularization Based Semi-supervised Domain Adaptation »
Hal Daumé III · Abhishek Kumar · Avishek Saha -
2009 Placeholder: Opening Remarks »
Richard Zemel -
2009 Poster: Multi-Step Dyna Planning for Policy Evaluation and Control »
Hengshuai Yao · Richard Sutton · Shalabh Bhatnagar · Dongcui Diao · Csaba Szepesvari -
2009 Poster: Convex Relaxation of Mixture Regression with Efficient Algorithms »
Novi Quadrianto · Tiberio Caetano · John Lim · Dale Schuurmans -
2009 Poster: Multi-Label Prediction via Sparse Infinite CCA »
Piyush Rai · Hal Daumé III -
2009 Poster: A General Projection Property for Distribution Families »
Yao-Liang Yu · Yuxi Li · Dale Schuurmans · Csaba Szepesvari -
2009 Poster: Unsupervised feature learning for audio classification using convolutional deep belief networks »
Honglak Lee · Peter Pham · Yan Largman · Andrew Y Ng -
2009 Poster: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2009 Spotlight: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2008 Poster: A computational model of hippocampal function in trace conditioning »
Elliot A Ludvig · Richard Sutton · Eric Verbeek · James Kehoe -
2008 Poster: Comparing model predictions of response bias and variance in cue combination »
Rama Natarajan · Iain Murray · Ladan Shams · Richard Zemel -
2008 Demonstration: RL-Glue: From Grid Worlds to Sensor Rich Robots »
Brian Tanner · Adam M White · Richard Sutton -
2008 Poster: Learning Hybrid Models for Image Annotation with Partially Labeled Data »
Xuming He · Richard Zemel -
2008 Poster: Large Margin Taxonomy Embedding for Document Categorization »
Kilian Q Weinberger · Olivier Chapelle -
2008 Poster: A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approxi »
Richard Sutton · Csaba Szepesvari · Hamid R Maei -
2008 Poster: Unsupervised Bayesian Parameter Estimation for Probabilistic Grammars »
Shay Cohen · Kevin Gimpel · Noah A Smith -
2008 Poster: Supervised Exponential Family Principal Component Analysis via Convex Optimizatio »
Yuhong Guo -
2008 Poster: Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression »
Piyush Rai · Hal Daumé III -
2008 Poster: Competing RBM density models for classification of fMRI images »
Tanya Schmah · Geoffrey E Hinton · Richard Zemel -
2008 Spotlight: Large Margin Taxonomy Embedding for Document Categorization »
Kilian Q Weinberger · Olivier Chapelle -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Poster: Sparse deep belief net model for visual area V2 »
Honglak Lee · Ekanadham Chaitanya · Andrew Y Ng -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Spotlight: Incremental Natural Actor-Critic Algorithms »
Shalabh Bhatnagar · Richard Sutton · Mohammad Ghavamzadeh · Mark P Lee -
2007 Spotlight: Stable Dual Dynamic Programming »
Tao Wang · Daniel Lizotte · Michael Bowling · Dale Schuurmans -
2007 Poster: Incremental Natural Actor-Critic Algorithms »
Shalabh Bhatnagar · Richard Sutton · Mohammad Ghavamzadeh · Mark P Lee -
2007 Poster: Stable Dual Dynamic Programming »
Tao Wang · Daniel Lizotte · Michael Bowling · Dale Schuurmans -
2007 Session: Spotlights »
Dale Schuurmans -
2007 Poster: Convex Relaxations of EM »
Yuhong Guo · Dale Schuurmans -
2007 Poster: Discriminative Batch Mode Active Learning »
Yuhong Guo · Dale Schuurmans -
2006 Workshop: Novel Applications of Dimensionality Reduction »
John Blitzer · Rajarshi Das · Irina Rish · Kilian Q Weinberger -
2006 Poster: Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields »
Chi-Hoon Lee · Shaojun Wang · Feng Jiao · Dale Schuurmans · Russell Greiner -
2006 Poster: implicit Online Learning with Kernels »
Li Cheng · Vishwanathan S V N · Dale Schuurmans · Shaojun Wang · Terry Caelli -
2006 Poster: Graph Regularization for Maximum Variance Unfolding with an Application to Sensor Localization »
Kilian Q Weinberger · Fei Sha · Qihui Zhu · Lawrence Saul -
2006 Poster: iLSTD: Convergence, Eligibility Traces, and Mountain Car »
Alborz Geramifard · Michael Bowling · Martin A Zinkevich · Richard Sutton -
2006 Poster: Efficient sparse coding algorithms, end-stopping and nCRF surround suppression »
Honglak Lee · Alexis Battle · Raina Rajat · Andrew Y Ng