Precup, Doina

212 publications

NeurIPS 2025 Capturing Individual Human Preferences with Reward Features Andre Barreto, Vincent Dumoulin, Yiran Mao, Mark Rowland, Nicolas Perez-Nieves, Bobak Shahriari, Yann Dauphin, Doina Precup, Hugo Larochelle

ICLRW 2025 Cracking the Code of Action: A Generative Approach to Affordances for Reinforcement Learning Lynn Cherif, Flemming Kondrup, David Venuto, Ankit Anand, Doina Precup, Khimya Khetarpal

ICLRW 2025 Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Samin Yeasar Arnob, Zhan Su, Minseon Kim, Oleksiy Ostapenko, Doina Precup, Lucas Caccia, Alessandro Sordoni

TMLR 2025 Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup

ICLR 2025 Langevin Soft Actor-Critic: Efficient Exploration Through Uncertainty-Driven Critic Learning Haque Ishfaq, Guangyuan Wang, Sami Nur Islam, Doina Precup

ICLR 2025 MaestroMotif: Skill Design from Artificial Intelligence Feedback Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro

NeurIPS 2025 Plasticity as the Mirror of Empowerment David Abel, Michael Bowling, Andre Barreto, Will Dabney, Shi Dong, Steven Stenberg Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

ICML 2025 Rejecting Hallucinated State Targets During Planning Harry Zhao, Tristan Sylvain, Romain Laroche, Doina Precup, Yoshua Bengio

ICLR 2025 Selective Unlearning via Representation Erasure Using Domain Adversarial Training Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle, Doina Precup, James J. Clark, Daniel M. Roy, Gintare Karolina Dziugaite

ICLR 2025 Training Language Models to Self-Correct via Reinforcement Learning Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, Aleksandra Faust

NeurIPS 2025 Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks Gavin McCracken, Gabriela Moisescu-Pareja, Vincent Létourneau, Doina Precup, Jonathan Love

NeurIPS 2024 Adaptive Exploration for Data-Efficient General Value Function Evaluations Arushi Jain, Josiah P. Hanna, Doina Precup

TMLR 2024 An Attentive Approach for Building Partial Reasoning Agents from Pixels Safa Alver, Doina Precup

ICML 2024 Code as Reward: Empowering Reinforcement Learning with VLMs David Venuto, Mohammad Sami Nur Islam, Martin Klissarov, Doina Precup, Sherry Yang, Ankit Anand

AISTATS 2024 Conditions on Preference Relations That Guarantee the Existence of Optimal Policies Jonathan Colaço Carr, Prakash Panangaden, Doina Precup

MLJ 2024 Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks Through Spectral Learning Tianyu Li, Doina Precup, Guillaume Rabusseau

ICLR 2024 Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning Harry Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio

NeurIPSW 2024 Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning Harry Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio

UAI 2024 Discrete Probabilistic Inference as Control in Multi-Path Environments Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio

NeurIPSW 2024 Effective Protein-Protein Interaction Exploration with PPIretrieval Chenqing Hua, Connor W. Coley, Guy Wolf, Doina Precup, Shuangjia Zheng

NeurIPS 2024 Efficient Reinforcement Learning by Discovering Neural Pathways Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Amy Zhang, Alessandro Sordoni, Doina Precup

NeurIPSW 2024 EnzymeFlow: Generating Reaction-Specific Enzyme Catalytic Pockets Through Flow Matching and Co-Evolutionary Dynamics Chenqing Hua, Yong Liu, Dinghuai Zhang, Odin Zhang, Sitao Luan, Kevin K Yang, Guy Wolf, Doina Precup, Shuangjia Zheng

IJCAI 2024 Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search Abbas Mehrabian, Ankit Anand, Hyunjik Kim, Nicolas Sonnerat, Matej Balog, Gheorghe Comanici, Tudor Berariu, Andrew Lee, Anian Ruoss, Anna Bulanova, Daniel Toyama, Sam Blackwell, Bernardino Romera-Paredes, Petar Velickovic, Laurent Orseau, Joonkyung Lee, Anurag Murty Naredla, Doina Precup, Adam Zsolt Wagner

ICMLW 2024 Functional Acceleration for Policy Mirror Descent Veronica Chelu, Doina Precup

NeurIPSW 2024 Identifying and Addressing Delusions for Target-Directed Decision Making Harry Zhao, Tristan Sylvain, Doina Precup, Yoshua Bengio

NeurIPS 2024 Learning Successor Features the Simple Way Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards, Doina Precup

NeurIPSW 2024 Mitigating Downstream Model Risks via Model Provenance Keyu Wang, Scott Schaffter, Abdullah Norozi Iranzad, Doina Precup, Jonathan Lebensold, Meg Risdal

ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL Johan Samir Obando Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

ICML 2024 Nash Learning from Human Feedback Remi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J Mankowitz, Doina Precup, Bilal Piot

NeurIPS 2024 Offline Multitask Representation Learning for Reinforcement Learning Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup

AISTATS 2024 On Learning History-Based Policies for Controlling Markov Decision Processes Gandharv Patil, Aditya Mahajan, Doina Precup

AISTATS 2024 On the Privacy of Selection Mechanisms with Gaussian Noise Jonathan Lebensold, Doina Precup, Borja Balle

NeurIPS 2024 Parseval Regularization for Continual Reinforcement Learning Wesley Chung, Lynn Cherif, David Meger, Doina Precup

CoLLAs 2024 Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup

JMLR 2024 Policy Gradient Methods in the Presence of Symmetries and State Abstractions Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup

ICLR 2024 Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli

NeurIPS 2024 QGFN: Controllable Greediness with Action Values Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio

ICMLW 2024 QGFN: Controllable Greediness with Action Values Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio

NeurIPS 2024 ReactZyme: A Benchmark for Enzyme-Reaction Prediction Chenqing Hua, Bozitao Zhong, Sitao Luan, Liang Hong, Guy Wolf, Doina Precup, Shuangjia Zheng

NeurIPS 2023 A Definition of Continual Reinforcement Learning David Abel, Andre Barreto, Benjamin Van Roy, Doina Precup, Hado P van Hasselt, Satinder P. Singh

ICMLW 2023 Accelerating Exploration and Representation Learning with Offline Pre-Training Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, Ankit Anand

ICMLW 2023 An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets Nikhil Murali Vemgal, Elaine Lau, Doina Precup

NeurIPSW 2023 DGFN: Double Generative Flow Networks Elaine Lau, Nikhil Murali Vemgal, Doina Precup, Emmanuel Bengio

NeurIPSW 2023 DGFN: Double Generative Flow Networks Elaine Lau, Nikhil Murali Vemgal, Doina Precup, Emmanuel Bengio

NeurIPSW 2023 Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search Abbas Mehrabian, Ankit Anand, Hyunjik Kim, Nicolas Sonnerat, Tudor Berariu, Matej Balog, Gheorghe Comanici, Andrew Lee, Anian Ruoss, Anna Bulanova, Daniel Toyama, Sam Blackwell, Bernardino Romera Paredes, Laurent Orseau, Petar Veličković, Anurag Murty Naredla, Joonkyung Lee, Adam Zsolt Wagner, Doina Precup

AISTATS 2023 Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation: Tail Averaging and Regularisation Gandharv Patil, Prashanth L.A., Dheeraj Nagaraj, Doina Precup

NeurIPS 2023 For SALE: State-Action Representation Learning for Deep Reinforcement Learning Scott Fujimoto, Wei-Di Chang, Edward Smith, Shixiang Gu, Doina Precup, David Meger

NeurIPSW 2023 Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels Thomas Jiralerspong, Flemming Kondrup, Doina Precup, Khimya Khetarpal

LoG 2023 MUDiff: Unified Diffusion for Complete Molecule Generation Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup

CoLLAs 2023 Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning Safa Alver, Doina Precup

ICML 2023 Multi-Environment Pretraining Enables Transfer to Action Limited Datasets David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum

ICLRW 2023 Multi-Environment Pretraining Enables Transfer to Action Limited Datasets David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum

ICMLW 2023 On Learning History-Based Policies for Controlling Markov Decision Processes Gandharv Patil, Aditya Mahajan, Doina Precup

AAAI 2023 On the Challenges of Using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects Sumana Basu, Marc-André Legault, Adriana Romero-Soriano, Doina Precup

NeurIPS 2023 Prediction and Control in Continual Reinforcement Learning Nishanth Anand, Doina Precup

JMLR 2023 Temporal Abstraction in Reinforcement Learning with the Successor Representation Marlos C. Machado, Andre Barreto, Doina Precup, Michael Bowling

AAAI 2023 Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning Flemming Kondrup, Thomas Jiralerspong, Elaine Lau, Nathan de Lara, Jacob Shkrob, My Duc Tran, Doina Precup, Sumana Basu

NeurIPS 2023 When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup

NeurIPSW 2022 Bayesian Q-Learning with Imperfect Expert Demonstrations Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek

NeurIPSW 2022 Bayesian Q-Learning with Imperfect Expert Demonstrations Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek

TMLR 2022 Behind the Machine’s Gaze: Neural Networks with Biologically-Inspired Constraints Exhibit Human-like Visual Attention Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

ICLR 2022 COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation Jongmin Lee, Cosmin Paduraru, Daniel J Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez

NeurIPSW 2022 Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks Sitao Luan, Harry Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup

ICLR 2022 Constructing a Good Behavior Basis for Transfer Using Generalized Policy Updates Safa Alver, Doina Precup

NeurIPS 2022 Continuous MDP Homomorphisms and Homomorphic Policy Gradient Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup

ICLRW 2022 Don't Freeze Your Embedding: Lessons from Policy Finetuning in Environment Transfer Victoria Dean, Daniel Kenji Toyama, Doina Precup

ICML 2022 Improving Robustness Against Real-World and Worst-Case Distribution Shifts Through Decision Region Quantification Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern Eskofier, Dario Zanca

JAIR 2022 Low-Rank Representation of Reinforcement Learning Policies Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau

NeurIPSW 2022 Multi-Environment Pretraining Enables Transfer to Action Limited Datasets David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum

IJCAI 2022 On the Expressivity of Markov Reward (Extended Abstract) David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh

ICLR 2022 Policy Gradients Incorporating the Future David Venuto, Elaine Lau, Doina Precup, Ofir Nachum

ICML 2022 Proving Theorems Using Incremental Learning and Hindsight Experience Replay Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen M Mcaleer, Vlad Firoiu, Lei M Zhang, Doina Precup, Shibl Mourad

NeurIPS 2022 Revisiting Heterophily for Graph Neural Networks Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup

NeurIPSW 2022 Simulating Human Gaze with Neural Visual Attention Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

NeurIPSW 2022 The Paradox of Choice: On the Role of Attention in Hierarchical Reinforcement Learning Andrei Cristian Nica, Khimya Khetarpal, Doina Precup

JAIR 2022 Towards Continual Reinforcement Learning: A Review and Perspectives Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

UAI 2022 Towards Painless Policy Optimization for Constrained MDPs Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup

ICML 2022 Why Should I Trust You, Bellman? the Bellman Error Is a Poor Replacement for Value Error Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu

NeurIPS 2021 A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio

NeurIPSW 2021 A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning Harry Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio

ICML 2021 A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation Scott Fujimoto, David Meger, Doina Precup

NeurIPS 2021 Flexible Option Learning Martin Klissarov, Doina Precup

NeurIPS 2021 Flow Network Based Generative Models for Non-Iterative Diverse Candidate Generation Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio

NeurIPS 2021 Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie

ICML 2021 Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup

NeurIPS 2021 On the Expressivity of Markov Reward David Abel, Will Dabney, Anna Harutyunyan, Mark K Ho, Michael L. Littman, Doina Precup, Satinder P. Singh

NeurIPSW 2021 Policy Gradients Incorporating the Future David Venuto, Elaine Lau, Doina Precup, Ofir Nachum

ICML 2021 Preferential Temporal Difference Learning Nishanth Anand, Doina Precup

ICML 2021 Randomized Exploration in Reinforcement Learning with General Value Function Approximation Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang

AAAI 2021 Self-Supervised Attention-Aware Reinforcement Learning Haiping Wu, Khimya Khetarpal, Doina Precup

NeurIPS 2021 Temporally Abstract Partial Models Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup

AAAI 2021 Variance Penalized On-Policy and Off-Policy Actor-Critic Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup

ICMLW 2020 A Brief Look at Generalization in Visual Meta-Reinforcement Learning Safa Alver, Doina Precup

AISTATS 2020 A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare

AAAI 2020 Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare

NeurIPS 2020 An Equivalence Between Loss Functions and Non-Uniform Sampling in Experience Replay Scott Fujimoto, David Meger, Doina Precup

ICMLW 2020 Attention Option-Critic Raviteja Chunduru, Doina Precup

AISTATS 2020 Efficient Planning Under Partial Observability with Unnormalized Q Functions and Spectral Learning Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau

NeurIPS 2020 Forethought and Hindsight in Credit Assignment Veronica Chelu, Doina Precup, Hado P van Hasselt

AAAI 2020 Gifting in Multi-Agent Reinforcement Learning (Student Abstract) Andrei Lupu, Doina Precup

ICML 2020 Interference and Generalization in Temporal Difference Learning Emmanuel Bengio, Joelle Pineau, Doina Precup

ICML 2020 Invariant Causal Prediction for Block MDPs Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

NeurIPS 2020 On Efficiency in Hierarchical Reinforcement Learning Zheng Wen, Doina Precup, Morteza Ibrahimi, Andre Barreto, Benjamin Van Roy, Satinder P. Singh

AAAI 2020 Options of Interest: Temporal Abstraction with Interest Functions Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup

NeurIPS 2020 Reward Propagation Using Graph Convolutional Networks Martin Klissarov, Doina Precup

IJCAI 2020 SVRG for Policy Evaluation with Fewer Gradient Evaluations Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup

AISTATS 2020 Value Preserving State-Action Abstractions David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael Littman

NeurIPS 2020 Value-Driven Hindsight Modelling Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess

ICML 2020 What Can I Do Here? a Theory of Affordances in Reinforcement Learning Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup

NeurIPS 2019 Break the Ceiling: Stronger Multi-Scale Deep Graph Convolutional Networks Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup

AAAI 2019 Combined Reinforcement Learning via Abstract Representations Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau

AISTATS 2019 Connecting Weighted Automata and Recurrent Neural Networks Through Spectral Learning Guillaume Rabusseau, Tianyu Li, Doina Precup

NeurIPS 2019 Hindsight Credit Assignment Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Remi Munos

AAAI 2019 Learning Options with Interest Functions Khimya Khetarpal, Doina Precup

ICLRW 2019 Learning Proposals for Sequential Importance Samplers Using Reinforced Variational Inference Zafarali Ahmed, Arjun Karuvally, Doina Precup, Simon Gravel

AAAI 2019 Leveraging Observations in Bandits: Between Risks and Benefits Andrei Lupu, Audrey Durand, Doina Precup

CoRL 2019 Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira E. Kahou, Joseph P. Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal

ICML 2019 Off-Policy Deep Reinforcement Learning Without Exploration Scott Fujimoto, David Meger, Doina Precup

ICML 2019 Per-Decision Option Discounting Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowe, Doina Precup

NeurIPS 2019 The Option Keyboard: Combining Skills in Reinforcement Learning Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup

AISTATS 2019 The Termination Critic Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup

ICML 2018 Convergent Tree Backup and Retrace with Function Approximation Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent

AAAI 2018 Deep Reinforcement Learning That Matters Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger

AAAI 2018 Imitation Upper Confidence Bound for Bandits on a Graph Andrei Lupu, Doina Precup

AAAI 2018 Learning Predictive State Representations from Non-Uniform Sampling Yuri Grinberg, Hossein Aboutalebi, Melanie Lyman-Abramovitch, Borja Balle, Doina Precup

AAAI 2018 Learning Robust Options Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor

NeurIPS 2018 Learning Safe Policies with Expert Guidance Jessie Huang, Fa Wu, Doina Precup, Yang Cai

AAAI 2018 Learning with Options That Terminate Off-Policy Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé

AISTATS 2018 Nonlinear Weighted Finite Automata Tianyu Li, Guillaume Rabusseau, Doina Precup

AAAI 2018 OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup

NeurIPS 2018 Temporal Regularization for Markov Decision Process Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup

AAAI 2018 When Waiting Is Not an Option: Learning Options with a Deliberation Cost Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup

IJCAI 2017 Approximate Value Iteration with Temporally Extended Actions (Extended Abstract) Timothy A. Mann, Shie Mannor, Doina Precup

ECML-PKDD 2017 Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting Di Wu, Boyu Wang, Doina Precup, Benoit Boulet

AAAI 2017 Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning Negar Ghourchian, Michel Allegue-Martínez, Doina Precup

AAAI 2017 The Option-Critic Architecture Pierre-Luc Bacon, Jean Harb, Doina Precup

ICML 2016 Differentially Private Policy Evaluation Borja Balle, Maziar Gomrokchi, Doina Precup

AAAI 2016 Incremental Stochastic Factorization for Online Reinforcement Learning André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup

IJCAI 2016 Learning Multi-Step Predictive State Representations Lucas Langer, Borja Balle, Doina Precup

JMLR 2016 Practical Kernel-Based Reinforcement Learning André M.S. Barreto, Doina Precup, Joelle Pineau

IJCAI 2015 An Expectation-Maximization Algorithm to Compute a Stochastic Factorization from Data André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup

JAIR 2015 Approximate Value Iteration with Temporally Extended Actions Timothy A. Mann, Shie Mannor, Doina Precup

NeurIPS 2015 Basis Refinement Strategies for Linear Value Function Approximation in MDPs Gheorghe Comanici, Doina Precup, Prakash Panangaden

NeurIPS 2015 Data Generation as Sequential Decision Making Philip Bachman, Doina Precup

UAI 2015 Learning and Planning with Timing Information in Markov Decision Processes Pierre-Luc Bacon, Borja Balle, Doina Precup

AAAI 2015 Representation Discovery for MDPs Using Bisimulation Metrics Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup

ICML 2015 Variational Generative Stochastic Networks with Collaborative Shaping Philip Bachman, Doina Precup

ICML 2014 A New Q(lambda) with Interim Forward View and Monte Carlo Equivalence Rich Sutton, Ashique Rupam Mahmood, Doina Precup, Hado Hasselt

UAI 2014 Bisimulation Metrics Are Optimal Value Functions Norman Ferns, Doina Precup

CVPR 2014 Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI Nagesh Subbanna, Doina Precup, Tal Arbel

NeurIPS 2014 Learning with Pseudo-Ensembles Philip Bachman, Ouais Alsharif, Doina Precup

NeurIPS 2014 Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg, Doina Precup, Michel Gendreau

JAIR 2014 Policy Iteration Based on Stochastic Factorization André da Motta Salles Barreto, Joelle Pineau, Doina Precup

ECCV 2014 Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel

ICML 2014 Sample-Based Approximate Regularization Philip Bachman, Amir-Massoud Farahmand, Doina Precup

ICML 2013 Average Reward Optimization Objective in Partially Observable Domains Yuri Grinberg, Doina Precup

NeurIPS 2013 Bellman Error Based Feature Generation Using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

ECML-PKDD 2013 Greedy Confidence Pursuit: A Pragmatic Approach to Multi-Bandit Optimization Philip Bachman, Doina Precup

NeurIPS 2013 Learning from Limited Demonstrations Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

AAAI 2012 Compressed Least-Squares Regression on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Joelle Pineau, Doina Precup

ICML 2012 Improved Estimation in Time Varying Models Doina Precup, Philip Bachman

AISTATS 2012 On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems Yuri Grinberg, Doina Precup

NeurIPS 2012 On-Line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau

CVPRW 2012 Soft Biometric Trait Classification from Real-World Face Videos Conditioned on Head Pose Estimation Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel

NeurIPS 2012 Value Pursuit Iteration Amir M. Farahmand, Doina Precup

ECML-PKDD 2011 Activity Recognition with Mobile Phones Jordan Frank, Shie Mannor, Doina Precup

AAAI 2011 Basis Function Discovery Using Spectral Clustering and Bisimulation Metrics Gheorghe Comanici, Doina Precup

AAAI 2011 Learning Compact Representations of Time-Varying Processes Philip Bachman, Doina Precup

NeurIPS 2011 Reinforcement Learning Using Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau

ACML 2010 A Study of Approximate Inference in Probabilistic Relational Models Fabian Kaelin, Doina Precup

AAAI 2010 Activity and Gait Recognition with Time-Delay Embeddings Jordan Frank, Shie Mannor, Doina Precup

ICML 2010 Approximate Predictive Representations of Partially Observable Systems Monica Dinculescu, Doina Precup

ECML-PKDD 2010 Smarter Sampling in Model-Based Bayesian Reinforcement Learning Pablo Samuel Castro, Doina Precup

AAAI 2010 Using Bisimulation for Policy Transfer in MDPs Pablo Samuel Castro, Doina Precup

NeurIPS 2009 Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation Hamid R. Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton

IJCAI 2009 Equivalence Relations in Fully and Partially Observable Markov Decision Processes Pablo Samuel Castro, Prakash Panangaden, Doina Precup

ICML 2009 Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora

ECML-PKDD 2009 Learning the Difference Between Partially Observable Dynamical Systems Sami Zhioua, Doina Precup, François Laviolette, Josée Desharnais

IJCAI 2009 Wikispeedia: An Online Game for Inferring Semantic Distances Between Concepts Robert West, Joelle Pineau, Doina Precup

NeurIPS 2008 Bounding Performance Loss in Approximate MDP Homomorphisms Jonathan Taylor, Doina Precup, Prakash Panagaden

ICML 2008 Reinforcement Learning in the Presence of Rare Events Jordan Frank, Shie Mannor, Doina Precup

IJCAI 2007 Context-Driven Predictions Marc G. Bellemare, Doina Precup

IJCAI 2007 Fast Image Alignment Using Anytime Algorithms Rupert Brooks, Tal Arbel, Doina Precup

IJCAI 2007 Using Linear Programming for Bayesian Exploration in Markov Decision Processes Pablo Samuel Castro, Doina Precup

ICML 2006 Automatic Basis Function Construction for Approximate Dynamic Programming and Reinforcement Learning Philipp W. Keller, Shie Mannor, Doina Precup

UAI 2006 Methods for Computing State Similarity in Markov Decision Processes Norm Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden

ECML-PKDD 2006 PAC-Learning of Markov Models with Hidden State Ricard Gavaldà, Philipp W. Keller, Joelle Pineau, Doina Precup

AAAI 2006 Representing Systems with Hidden State Christopher Hundt, Prakash Panangaden, Joelle Pineau, Doina Precup

ECML-PKDD 2005 Active Learning in Partially Observable Markov Decision Processes Robin Jaulmes, Joelle Pineau, Doina Precup

UAI 2005 Metrics for Markov Decision Processes with Infinite State Spaces Norm Ferns, Prakash Panangaden, Doina Precup

IJCAI 2005 Model Minimization by Linear PSR Masoumeh T. Izadi, Doina Precup

NeurIPS 2005 Off-Policy Learning with Options and Recognizers Doina Precup, Cosmin Paduraru, Anna Koop, Richard S. Sutton, Satinder P. Singh

IJCAI 2005 Using Core Beliefs for Point-Based Value Iteration Masoumeh T. Izadi, Ajit V. Rajwade, Doina Precup

ECML-PKDD 2005 Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes Masoumeh T. Izadi, Doina Precup

MLJ 2004 Classification Using Phi-Machines and Constructive Function Approximation Doina Precup, Paul E. Utgoff

AAAI 2004 Metrics for Finite Markov Decision Processes Norm Ferns, Prakash Panangaden, Doina Precup

UAI 2004 Metrics for Finite Markov Decision Processes Norm Ferns, Prakash Panangaden, Doina Precup

ECML-PKDD 2004 Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning Bohdana Ratitch, Doina Precup

IJCAI 2003 A Planning Algorithm for Predictive State Representations Masoumeh T. Izadi, Doina Precup

ICML 2003 Combining TD-Learning with Cascade-Correlation Networks François Rivest, Doina Precup

ECML-PKDD 2003 Using MDP Characteristics to Guide Exploration in Reinforcement Learning Bohdana Ratitch, Doina Precup

NeurIPS 2002 A Convergent Form of Approximate Policy Iteration Theodore J. Perkins, Doina Precup

ECML-PKDD 2002 Characterizing Markov Decision Processes Bohdana Ratitch, Doina Precup

ICML 2001 Off-Policy Temporal Difference Learning with Function Approximation Doina Precup, Richard S. Sutton, Sanjoy Dasgupta

ICML 2000 Eligibility Traces for Off-Policy Policy Evaluation Doina Precup, Richard S. Sutton, Satinder Singh

ICML 1998 Classification Using Phi-Machines and Constructive Function Approximation Doina Precup, Paul E. Utgoff

NeurIPS 1998 Improved Switching Among Temporally Abstract Actions Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran

ICML 1998 Intra-Option Learning About Temporally Abstract Actions Richard S. Sutton, Doina Precup, Satinder Singh

ECML-PKDD 1998 Theoretical Results on Reinforcement Learning with Temporally Abstract Options Doina Precup, Richard S. Sutton, Satinder Singh

ICML 1997 Exponentiated Gradient Methods for Reinforcement Learning Doina Precup, Richard S. Sutton

NeurIPS 1997 Learning to Schedule Straight-Line Code J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Doina Precup, Darko Stefanovic, Carla E. Brodley, David Scheeff

NeurIPS 1997 Multi-Time Models for Temporally Abstract Planning Doina Precup, Richard S. Sutton