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OVERVIEW This workshop aims to gather researchers in the area of sequential decision making to discuss recent findings and new challenges around the concept of model misspecification. A misspecified model is a model that either (1) cannot be tractably solved, (2) solving the model does not produce an acceptable solution for the target problem, or (3) the model clearly does not describe the available data perfectly. However, even though the model has its issues, we are interested in finding a good policy. The question is thus: How can misspecified models be made to lead to good policies?
We refer to the following (non exhaustive) types of misspecification.
1. States and Context. A misspecified state representation relates to research problems such as Hidden Markov Models, Predictive State Representations, Feature Reinforcement Learning, Partially Observable Markov Decision Problems, etc. The related question of misspecified context in contextual bandits is also relevant.
2. Dynamics. Consider learning a policy for a class of several MDPs rather than a single MDP, or optimizing a risk averse (as opposed to expected) objective. These approaches could be used to derive a reasonable policy for the target MDP even if the model we solved to obtain it is misspecified. Thus, robustness, safety, and risk-aversion are examples of relevant approaches to this question.
3. Actions. The underlying insight of working with high-level actions built on top of lower-level actions is that if we had the right high-level actions, we would have faster learning/planning. However, finding an appropriate set of high-level actions can be difficult. One form of model misspecification occurs when the given high-level actions cannot be combined to derive an acceptable policy.
More generally, since misspecification may slow learning or prevent an algorithm from finding any acceptable solution, improving the efficiency of planning and learning methods under misspecification is of primary importance. At another level, all these challenges can benefit greatly from the identification of finer properties of MDPs (local recoverability, etc.) and better notions of complexity. These questions are deeply rooted in theory and in recent applications in fields diverse as air-traffic control, marketing, and robotics. We thus also want to encourage presentations of challenges that provide a red-line and agenda for future research, or a survey of the current achievements and difficulties. This includes concrete problems like Energy management, Smart grids, Computational sustainability and Recommender systems.
We welcome contributions on these exciting questions, with the goals of (1) helping close the gap between strong theoretical guarantees and challenging application requirements, (2) identifying promising directions of near future research, for both applications and theory of sequential decision making, and (3) triggering collaborations amongst researchers on learning good policies despite being given misspecified models.
MOTIVATION, OBJECTIVES Despite the success of sequential decision making theory at providing solutions to challenging settings, the field faces a limitation. Often strong theoretical guarantees depend on the assumption that a solution to the class of models considered is a good solution to the target problem. A popular example is that of finite-state MDP learning for which the model of the state-space is assumed known. Such an assumption is however rarely met in practice. Similarly, in recommender systems and contextual bandits, the context may not capture an accurate summary of the users. Developing a methodology for finding, estimating, and dealing with the limitations of the model is paramount to the success of sequential decision processes. Another example of model misspecification occurs in Hierarchical Reinforcement Learning: In many real-world applications, we could solve the problem easily if we had the right set of high-level actions. Instead, we need to find a way to build those from a cruder set of primitive actions or existing high-level actions that do not suit the current task.
Yet another applicative challenge is when we face a process that can only be modeled as an MDP evolving in some class of MDPs, instead of a fixed MDP. leading to robust reinforcement learning, or when we call for safety or risk-averse guarantees.
These problems are important bottlenecks standing in the way of applying sequential decision making to challenging application, and motivate the triple goal of this workshop.
RELEVANCE TO THE COMMUNITY Misspecification of models (in the senses we consider here) is an important problem that is faced in many – if not all – real-world applications of sequential decision making under uncertainty. While theoretical results have primarily focused on the case when models of the environment are well-specified, little work has been done on extending the theory to the case of misspecification. Attempting at understanding why and when incorrectly specified models lead to good empirical performance beyond what the current theory explains is also an important goal. We believe that this workshop will be of great interest for both theoreticians and applied researchers in the field.
PAPER SUBMISSIONS The workshop aims to spark vibrant discussion with talks from invited speakers, presentations from authors of accepted papers, and a poster session. We are soliciting two types of contributions:
• Papers (4-6 pages) for oral or interactive poster presentations
• Extended abstracts (2 pages) for interactive poster presentation
We encourage submissions from different fields of sequential decision making (e.g., reinforcement learning, online learning, active learning), as well as application-domain experts (from e.g., digital marketing, recommender systems, personalized medicine, etc.) addressing the following (non-
exhaustive) list of questions and topics:
• Misspecification in model selection.
• State-representations in Reinforcement learning: Hidden Markov Models, Predictive State Representations, Feature Reinforcement Learning, Partially Observable Markov Decision Processes.
• Latent variables in sequential decision making and techniques to handle them.
• Robustness, Safety and Risk-aversion in Reinforcement Learning.
• Curiosity and Autonomous learning (reward misspecification).
• Reinforcement Learning with Options.
• Application for the Reinforcement Learning community (Computational Sustainability, Smart Cities, Smart grids, etc.).
• Other topics whose relevance to the workshop is well supported.
Solutions to such challenges will benefit the machine learning community at large, since they also appear in many real-world applications.
Author Information
Odalric-Ambrym Maillard (INRIA)
Timothy A Mann (The Technion)
Shie Mannor (Technion)
Jeremie Mary (INRIA / Univ. Lille)
Laurent Orseau (AgroParisTech/INRA)
Thomas Dietterich (Oregon State University)
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces. Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.
Ronald Ortner (Montanuniversitaet Leoben)
Peter Grünwald (CWI and Leiden University)
Joelle Pineau (McGill University)
Joelle Pineau is an Associate Professor and William Dawson Scholar at McGill University where she co-directs the Reasoning and Learning Lab. She also leads the Facebook AI Research lab in Montreal, Canada. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is currently President of the International Machine Learning Society. She is a recipient of NSERC's E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR) and in 2016 was named a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada.
Raphael Fonteneau (Université de Liège)
Georgios Theocharous (Adobe Research)
Esteban D Arcaute (@WalmartLabs)
Christos Dimitrakakis (University of Oslo)
Nan Jiang (University of Illinois at Urbana-Champaign)
Doina Precup (McGill University / Mila / DeepMind Montreal)
Pierre-Luc Bacon (McGill University)
Marek Petrik (University of New Hampshire)
Aviv Tamar (Technion)
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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 Presentations »
Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange -
2019 : Poster Spotlight 2 »
Aaron Sidford · Mengdi Wang · Lin Yang · Yinyu Ye · Zuyue Fu · Zhuoran Yang · Yongxin Chen · Zhaoran Wang · Ofir Nachum · Bo Dai · Ilya Kostrikov · Dale Schuurmans · Ziyang Tang · Yihao Feng · Lihong Li · Denny Zhou · Qiang Liu · Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Simon Du · Sham Kakade · Ruosong Wang · Minshuo Chen · Tianyi Liu · Xingguo Li · Zhaoran Wang · Tuo Zhao · Philip Amortila · Doina Precup · Prakash Panangaden · Marc Bellemare -
2019 : Panel Discussion »
Richard Sutton · Doina Precup -
2019 : Poster and Coffee Break 1 »
Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova -
2019 : Adaptive Trust Region Policy Optimization: Convergence and Faster Rates of regularized MDPs »
Lior Shani · Yonathan Efroni · Shie Mannor -
2019 : Invited Talk: Hierarchical Reinforcement Learning: Computational Advances and Neuroscience Connections »
Doina Precup -
2019 : Panel Discussion led by Grace Lindsay »
Grace Lindsay · Blake Richards · Doina Precup · Jacqueline Gottlieb · Jeff Clune · Jane Wang · Richard Sutton · Angela Yu · Ida Momennejad -
2019 : Opening Remarks »
Raymond Chua · Feryal Behbahani · Sara Zannone · Rui Ponte Costa · Claudia Clopath · Doina Precup · Blake Richards -
2019 : Aviv Tamar: Visual Plan Imagination - An Interpretable Robot Learning Framework »
Aviv Tamar -
2019 Workshop: Biological and Artificial Reinforcement Learning »
Raymond Chua · Sara Zannone · Feryal Behbahani · Rui Ponte Costa · Claudia Clopath · Blake Richards · Doina Precup -
2019 Workshop: Retrospectives: A Venue for Self-Reflection in ML Research »
Ryan Lowe · Yoshua Bengio · Joelle Pineau · Michela Paganini · Jessica Forde · Shagun Sodhani · Abhishek Gupta · Joel Lehman · Peter Henderson · Kanika Madan · Koustuv Sinha · Xavier Bouthillier -
2019 Workshop: Safety and Robustness in Decision-making »
Mohammad Ghavamzadeh · Shie Mannor · Yisong Yue · Marek Petrik · Yinlam Chow -
2019 Poster: PAC-Bayes Un-Expected Bernstein Inequality »
Zakaria Mhammedi · Peter Grünwald · Benjamin Guedj -
2019 Poster: Budgeted Reinforcement Learning in Continuous State Space »
Nicolas Carrara · Edouard Leurent · Romain Laroche · Tanguy Urvoy · Odalric-Ambrym Maillard · Olivier Pietquin -
2019 Poster: No-Press Diplomacy: Modeling Multi-Agent Gameplay »
Philip Paquette · Yuchen Lu · SETON STEVEN BOCCO · Max Smith · Satya O.-G. · Jonathan K. Kummerfeld · Joelle Pineau · Satinder Singh · Aaron Courville -
2019 Poster: Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks »
Sitao Luan · Mingde Zhao · Xiao-Wen Chang · Doina Precup -
2019 Poster: Learning Multiple Markov Chains via Adaptive Allocation »
Mohammad Sadegh Talebi · Odalric-Ambrym Maillard -
2019 Poster: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor -
2019 Spotlight: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor -
2019 Poster: Provably Efficient Q-Learning with Low Switching Cost »
Yu Bai · Tengyang Xie · Nan Jiang · Yu-Xiang Wang -
2019 Poster: Regret Bounds for Learning State Representations in Reinforcement Learning »
Ronald Ortner · Matteo Pirotta · Alessandro Lazaric · Ronan Fruit · Odalric-Ambrym Maillard -
2018 : Joelle Pineau »
Joelle Pineau -
2018 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · David Silver · Satinder Singh · Joelle Pineau · Joshua Achiam · Rein Houthooft · Aravind Srinivas -
2018 Poster: Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning »
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor -
2018 Spotlight: Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning »
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor -
2018 Poster: Temporal Regularization for Markov Decision Process »
Pierre Thodoroff · Audrey Durand · Joelle Pineau · Doina Precup -
2018 Poster: Scalar Posterior Sampling with Applications »
Georgios Theocharous · Zheng Wen · Yasin Abbasi Yadkori · Nikos Vlassis -
2018 Poster: On Oracle-Efficient PAC RL with Rich Observations »
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2018 Poster: Learning Safe Policies with Expert Guidance »
Jessie Huang · Fa Wu · Doina Precup · Yang Cai -
2018 Spotlight: On Oracle-Efficient PAC RL with Rich Observations »
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2018 Invited Talk: Reproducible, Reusable, and Robust Reinforcement Learning »
Joelle Pineau -
2018 Poster: Completing State Representations using Spectral Learning »
Nan Jiang · Alex Kulesza · Satinder Singh -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : Progress on Deep Reinforcement Learning with Temporal Abstraction (Doina Precup) »
Doina Precup -
2017 : Doina Precup »
Doina Precup -
2017 : Peter Grünwald - A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity »
Peter Grünwald -
2017 Workshop: Hierarchical Reinforcement Learning »
Andrew G Barto · Doina Precup · Shie Mannor · Tom Schaul · Roy Fox · Carlos Florensa -
2017 : Invited Talk - Joelle Pineau »
Joelle Pineau -
2017 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Abhishek Das · Satwik Kottur · Stefan Lee · Mateusz Malinowski · Olivier Pietquin · Devi Parikh · Dhruv Batra · Aaron Courville · Jeremie Mary -
2017 Poster: Repeated Inverse Reinforcement Learning »
Kareem Amin · Nan Jiang · Satinder Singh -
2017 Poster: Rotting Bandits »
Nir Levine · Yacov Crammer · Shie Mannor -
2017 Poster: Multi-View Decision Processes: The Helper-AI Problem »
Christos Dimitrakakis · David Parkes · Goran Radanovic · Paul Tylkin -
2017 Spotlight: Repeated Inverse Reinforcement Learning »
Kareem Amin · Nan Jiang · Satinder Singh -
2017 Demonstration: A Deep Reinforcement Learning Chatbot »
Iulian Vlad Serban · Chinnadhurai Sankar · Mathieu Germain · Saizheng Zhang · Zhouhan Lin · Sandeep Subramanian · Taesup Kim · Michael Pieper · Sarath Chandar · Nan Rosemary Ke · Sai Rajeswar Mudumba · Alexandre de Brébisson · Jose Sotelo · Dendi A Suhubdy · Vincent Michalski · Joelle Pineau · Yoshua Bengio -
2017 Poster: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Spotlight: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Poster: Multitask Spectral Learning of Weighted Automata »
Guillaume Rabusseau · Borja Balle · Joelle Pineau -
2017 Poster: Shallow Updates for Deep Reinforcement Learning »
Nir Levine · Tom Zahavy · Daniel J Mankowitz · Aviv Tamar · Shie Mannor -
2016 : Joelle Pineau »
Joelle Pineau -
2016 : Automated Data Cleaning via Multi-View Anomaly Detection »
Thomas Dietterich -
2016 : Safe Probability »
Peter Grünwald -
2016 : (Ir-)rationality of human decision making »
Peter Grünwald -
2016 Workshop: The Future of Interactive Machine Learning »
Kory Mathewson @korymath · Kaushik Subramanian · Mark Ho · Robert Loftin · Joseph L Austerweil · Anna Harutyunyan · Doina Precup · Layla El Asri · Matthew Gombolay · Jerry Zhu · Sonia Chernova · Charles Isbell · Patrick M Pilarski · Weng-Keen Wong · Manuela Veloso · Julie A Shah · Matthew Taylor · Brenna Argall · Michael Littman -
2016 Poster: Safe Policy Improvement by Minimizing Robust Baseline Regret »
Mohammad Ghavamzadeh · Marek Petrik · Yinlam Chow -
2016 Poster: Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning »
Wouter Koolen · Peter Grünwald · Tim van Erven -
2016 Poster: Adaptive Skills Adaptive Partitions (ASAP) »
Daniel J Mankowitz · Timothy A Mann · Shie Mannor -
2015 : Discussion Panel »
Tim van Erven · Wouter Koolen · Peter Grünwald · Shai Ben-David · Dylan Foster · Satyen Kale · Gergely Neu -
2015 : Between stochastic and adversarial: forecasting with online ARMA models »
Shie Mannor -
2015 : Easy Data »
Peter Grünwald -
2015 Workshop: Machine Learning for (e-)Commerce »
Esteban Arcaute · Mohammad Ghavamzadeh · Shie Mannor · Georgios Theocharous -
2015 Poster: Online Learning for Adversaries with Memory: Price of Past Mistakes »
Oren Anava · Elad Hazan · Shie Mannor -
2015 Poster: Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach »
Yinlam Chow · Aviv Tamar · Shie Mannor · Marco Pavone -
2015 Poster: Policy Gradient for Coherent Risk Measures »
Aviv Tamar · Yinlam Chow · Mohammad Ghavamzadeh · Shie Mannor -
2015 Poster: Policy Evaluation Using the Ω-Return »
Philip Thomas · Scott Niekum · Georgios Theocharous · George Konidaris -
2015 Poster: Community Detection via Measure Space Embedding »
Mark Kozdoba · Shie Mannor -
2015 Poster: Data Generation as Sequential Decision Making »
Philip Bachman · Doina Precup -
2015 Spotlight: Data Generation as Sequential Decision Making »
Philip Bachman · Doina Precup -
2015 Poster: Basis refinement strategies for linear value function approximation in MDPs »
Gheorghe Comanici · Doina Precup · Prakash Panangaden -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: Autonomously Learning Robots »
Gerhard Neumann · Joelle Pineau · Peter Auer · Marc Toussaint -
2014 Poster: "How hard is my MDP?" The distribution-norm to the rescue »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor -
2014 Poster: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Yuri Grinberg · Doina Precup · Michel Gendreau -
2014 Poster: RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning »
Marek Petrik · Dharmashankar Subramanian -
2014 Poster: Learning with Pseudo-Ensembles »
Philip Bachman · Ouais Alsharif · Doina Precup -
2014 Poster: Robust Logistic Regression and Classification »
Jiashi Feng · Huan Xu · Shie Mannor · Shuicheng Yan -
2014 Demonstration: SmartWheeler – A smart robotic wheelchair platform »
Martin Gerdzhev · Joelle Pineau · Angus Leigh · Andrew Sutcliffe -
2014 Spotlight: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Yuri Grinberg · Doina Precup · Michel Gendreau -
2014 Spotlight: RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning »
Marek Petrik · Dharmashankar Subramanian -
2014 Oral: "How hard is my MDP?" The distribution-norm to the rescue »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor -
2014 Poster: Learning the Learning Rate for Prediction with Expert Advice »
Wouter M Koolen · Tim van Erven · Peter Grünwald -
2013 Workshop: Machine Learning for Sustainability »
Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao -
2013 Workshop: Learning Faster From Easy Data »
Peter Grünwald · Wouter M Koolen · Sasha Rakhlin · Nati Srebro · Alekh Agarwal · Karthik Sridharan · Tim van Erven · Sebastien Bubeck -
2013 Poster: Reinforcement Learning in Robust Markov Decision Processes »
Shiau Hong Lim · Huan Xu · Shie Mannor -
2013 Poster: Learning from Limited Demonstrations »
Beomjoon Kim · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Poster: Bellman Error Based Feature Generation using Random Projections on Sparse Spaces »
Mahdi Milani Fard · Yuri Grinberg · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Spotlight: Learning from Limited Demonstrations »
Beomjoon Kim · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Poster: Online PCA for Contaminated Data »
Jiashi Feng · Huan Xu · Shie Mannor · Shuicheng Yan -
2013 Poster: Learning Multiple Models via Regularized Weighting »
Daniel Vainsencher · Shie Mannor · Huan Xu -
2013 Poster: Aggregating Optimistic Planning Trees for Solving Markov Decision Processes »
Gunnar Kedenburg · Raphael Fonteneau · Remi Munos -
2012 Workshop: Human Computation for Science and Computational Sustainability »
Theodoros Damoulas · Thomas Dietterich · Edith Law · Serge Belongie -
2012 Poster: Value Pursuit Iteration »
Amir-massoud Farahmand · Doina Precup -
2012 Poster: Online Regret Bounds for Undiscounted Continuous Reinforcement Learning »
Ronald Ortner · Daniil Ryabko -
2012 Poster: Reducing statistical time-series problems to binary classification »
Daniil Ryabko · Jeremie Mary -
2012 Poster: Probabilistic Topic Coding for Superset Label Learning »
Liping Liu · Thomas Dietterich -
2012 Poster: Mixability in Statistical Learning »
Tim van Erven · Peter Grünwald · Mark Reid · Robert Williamson -
2012 Invited Talk: Challenges for Machine Learning in Computational Sustainability »
Thomas Dietterich -
2012 Poster: Online allocation and homogeneous partitioning for piecewise constant mean-approximation »
Alexandra Carpentier · Odalric-Ambrym Maillard -
2012 Poster: On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization »
Andre S Barreto · Doina Precup · Joelle Pineau -
2012 Poster: The Perturbed Variation »
Maayan Harel · Shie Mannor -
2012 Poster: Hierarchical Optimistic Region Selection driven by Curiosity »
Odalric-Ambrym Maillard -
2011 Workshop: Machine Learning for Sustainability »
Thomas Dietterich · J. Zico Kolter · Matthew A Brown -
2011 Poster: From Bandits to Experts: On the Value of Side-Observations »
Shie Mannor · Ohad Shamir -
2011 Session: Oral Session 10 »
Joelle Pineau -
2011 Spotlight: From Bandits to Experts: On the Value of Side-Observations »
Shie Mannor · Ohad Shamir -
2011 Poster: Adaptive Hedge »
Tim van Erven · Peter Grünwald · Wouter M Koolen · Steven D Rooij -
2011 Poster: PAC-Bayesian Analysis of Contextual Bandits »
Yevgeny Seldin · Peter Auer · Francois Laviolette · John Shawe-Taylor · Ronald Ortner -
2011 Poster: Selecting the State-Representation in Reinforcement Learning »
Odalric-Ambrym Maillard · Remi Munos · Daniil Ryabko -
2011 Poster: Sparse Recovery with Brownian Sensing »
Alexandra Carpentier · Odalric-Ambrym Maillard · Remi Munos -
2011 Poster: Collective Graphical Models »
Daniel Sheldon · Thomas Dietterich -
2011 Poster: Committing Bandits »
Loc X Bui · Ramesh Johari · Shie Mannor -
2011 Poster: Inverting Grice's Maxims to Learn Rules from Natural Language Extractions »
M. Shahed Sorower · Thomas Dietterich · Janardhan Rao Doppa · Walker Orr · Prasad Tadepalli · Xiaoli Fern -
2011 Poster: Reinforcement Learning using Kernel-Based Stochastic Factorization »
Andre S Barreto · Doina Precup · Joelle Pineau -
2010 Workshop: Learning and Planning from Batch Time Series Data »
Daniel Lizotte · Michael Bowling · Susan Murphy · Joelle Pineau · Sandeep Vijan -
2010 Spotlight: Online Classification with Specificity Constraints »
Andrey Bernstein · Shie Mannor · Nahum Shimkin -
2010 Poster: Online Classification with Specificity Constraints »
Andrey Bernstein · Shie Mannor · Nahum Shimkin -
2010 Poster: Distributionally Robust Markov Decision Processes »
Huan Xu · Shie Mannor -
2010 Spotlight: LSTD with Random Projections »
Mohammad Ghavamzadeh · Alessandro Lazaric · Odalric-Ambrym Maillard · Remi Munos -
2010 Poster: LSTD with Random Projections »
Mohammad Ghavamzadeh · Alessandro Lazaric · Odalric-Ambrym Maillard · Remi Munos -
2010 Poster: PAC-Bayesian Model Selection for Reinforcement Learning »
Mahdi Milani Fard · Joelle Pineau -
2010 Poster: Scrambled Objects for Least-Squares Regression »
Odalric-Ambrym Maillard · Remi Munos -
2009 Mini Symposium: Machine Learning for Sustainability »
J. Zico Kolter · Thomas Dietterich · Andrew Y Ng -
2009 Poster: Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability »
Keith Bush · Joelle Pineau -
2009 Poster: Compressed Least-Squares Regression »
Odalric-Ambrym Maillard · Remi Munos -
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: MDPs with Non-Deterministic Policies »
Mahdi Milani Fard · Joelle Pineau -
2008 Poster: Bounding Performance Loss in Approximate MDP Homomorphisms »
Doina Precup · Jonathan Taylor Taylor · Prakash Panangaden -
2007 Spotlight: Bayes-Adaptive POMDPs »
Stephane Ross · Brahim Chaib-draa · Joelle Pineau -
2007 Spotlight: Catching Up Faster in Bayesian Model Selection and Model Averaging »
Tim van Erven · Peter Grünwald · Steven de Rooij -
2007 Poster: Catching Up Faster in Bayesian Model Selection and Model Averaging »
Tim van Erven · Peter Grünwald · Steven de Rooij -
2007 Poster: Bayes-Adaptive POMDPs »
Stephane Ross · Brahim Chaib-draa · Joelle Pineau -
2007 Poster: Theoretical Analysis of Heuristic Search Methods for Online POMDPs »
Stephane Ross · Joelle Pineau · Brahim Chaib-draa