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ORCIDDoina Precup
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- affiliation: McGill University, Montreal, Canada
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2020 – today
- 2025
[c251]Haque Ishfaq, Guangyuan Wang, Sami Nur Islam, Doina Precup:
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning. ICLR 2025- [c250]Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro:
MaestroMotif: Skill Design from Artificial Intelligence Feedback. ICLR 2025 - [c249]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 M. P. Behbahani, Aleksandra Faust:
Training Language Models to Self-Correct via Reinforcement Learning. ICLR 2025 - [c248]Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle, Doina Precup, James J. Clark, Daniel M. Roy, Gintare Karolina Dziugaite:
Selective Unlearning via Representation Erasure Using Domain Adversarial Training. ICLR 2025 - [i193]Haque Ishfaq, Guangyuan Wang, Sami Nur Islam, Doina Precup:
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning. CoRR abs/2501.17827 (2025) - [i192]David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh:
Agency Is Frame-Dependent. CoRR abs/2502.04403 (2025) - [i191]André Barreto, Vincent Dumoulin, Yiran Mao, Nicolas Perez Nieves, Bobak Shahriari, Yann N. Dauphin, Doina Precup, Hugo Larochelle:
Capturing Individual Human Preferences with Reward Features. CoRR abs/2503.17338 (2025) - [i190]Lynn Cherif, Flemming Kondrup, David Venuto, Ankit Anand, Doina Precup, Khimya Khetarpal:
Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning. CoRR abs/2504.17282 (2025) - 2024
- [j42]Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu:
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. RLJ 3: 1211-1235 (2024) - [j41]Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup:
Policy Gradient Methods in the Presence of Symmetries and State Abstractions. J. Mach. Learn. Res. 25: 71:1-71:57 (2024) - [j40]Tianyu Li, Doina Precup, Guillaume Rabusseau
:
Connecting weighted automata, tensor networks and recurrent neural networks through spectral learning. Mach. Learn. 113(5): 2619-2653 (2024) - [c247]Jonathan Lebensold, Doina Precup, Borja Balle:
On the Privacy of Selection Mechanisms with Gaussian Noise. AISTATS 2024: 1495-1503 - [c246]Gandharv Patil, Aditya Mahajan, Doina Precup:
On learning history-based policies for controlling Markov decision processes. AISTATS 2024: 3511-3519 - [c245]Jonathan Colaço Carr, Prakash Panangaden, Doina Precup:
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies. AISTATS 2024: 3916-3924 - [c244]Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup:
Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents. CoLLAs 2024: 959-977 - [c243]David Budaghyan, Charles C. Onu, Arsenii Gorin, Cem Subakan, Doina Precup:
CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds. ICASSP 2024: 11966-11970 - [c242]Harry Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio:
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning. ICLR 2024 - [c241]Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli:
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. ICLR 2024 - [c240]Rémi 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:
Nash Learning from Human Feedback. ICML 2024 - [c239]Johan Samir Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro:
Mixtures of Experts Unlock Parameter Scaling for Deep RL. ICML 2024 - [c238]David Venuto, Mohammad Sami Nur Islam, Martin Klissarov, Doina Precup, Sherry Yang, Ankit Anand:
Code as Reward: Empowering Reinforcement Learning with VLMs. ICML 2024 - [c237]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:
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search. IJCAI 2024: 6985-6993 - [c236]Samin Yeasar Arnob, Riyasat Ohib, Sergey M. Plis, Amy Zhang, Alessandro Sordoni, Doina Precup:
Efficient Reinforcement Learning by Discovering Neural Pathways. NeurIPS 2024 - [c235]Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards, Doina Precup:
Learning Successor Features the Simple Way. NeurIPS 2024 - [c234]Wesley Chung, Lynn Cherif, Doina Precup, David Meger:
Parseval Regularization for Continual Reinforcement Learning. NeurIPS 2024 - [c233]Chenqing Hua, Bozitao Zhong, Sitao Luan, Liang Hong, Guy Wolf, Doina Precup, Shuangjia Zheng:
ReactZyme: A Benchmark for Enzyme-Reaction Prediction. NeurIPS 2024 - [c232]Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup:
Offline Multitask Representation Learning for Reinforcement Learning. NeurIPS 2024 - [c231]Arushi Jain, Josiah Hanna, Doina Precup:
Adaptive Exploration for Data-Efficient General Value Function Evaluations. NeurIPS 2024 - [c230]Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio:
QGFN: Controllable Greediness with Action Values. NeurIPS 2024 - [c229]Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio:
Discrete Probabilistic Inference as Control in Multi-path Environments. UAI 2024: 997-1021 - [i189]Chenqing Hua, Connor W. Coley, Guy Wolf, Doina Precup, Shuangjia Zheng:
Effective Protein-Protein Interaction Exploration with PPIretrieval. CoRR abs/2402.03675 (2024) - [i188]David Venuto, Mohammad Sami Nur Islam, Martin Klissarov, Doina Precup, Sherry Yang, Ankit Anand:
Code as Reward: Empowering Reinforcement Learning with VLMs. CoRR abs/2402.04764 (2024) - [i187]Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio:
QGFN: Controllable Greediness with Action Values. CoRR abs/2402.05234 (2024) - [i186]Jonathan Lebensold, Doina Precup, Borja Balle:
On the Privacy of Selection Mechanisms with Gaussian Noise. CoRR abs/2402.06137 (2024) - [i185]Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro:
Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR abs/2402.08609 (2024) - [i184]Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio:
Discrete Probabilistic Inference as Control in Multi-path Environments. CoRR abs/2402.10309 (2024) - [i183]Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup:
Offline Multitask Representation Learning for Reinforcement Learning. CoRR abs/2403.11574 (2024) - [i182]Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio:
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR abs/2405.01616 (2024) - [i181]Arushi Jain, Josiah P. Hanna, Doina Precup:
Adaptive Exploration for Data-Efficient General Value Function Evaluations. CoRR abs/2405.07838 (2024) - [i180]Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup:
Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents. CoRR abs/2405.16899 (2024) - [i179]Jordi Armengol-Estapé, Vincent Michalski, Ramnath Kumar, Pierre-Luc St-Charles, Doina Precup, Samira Ebrahimi Kahou:
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization. CoRR abs/2405.18751 (2024) - [i178]Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu:
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. CoRR abs/2406.12241 (2024) - [i177]Sitao Luan, Chenqing Hua, Qincheng Lu, Liheng Ma, Lirong Wu, Xinyu Wang, Minkai Xu, Xiao-Wen Chang, Doina Precup, Rex Ying, Stan Z. Li, Jian Tang, Guy Wolf, Stefanie Jegelka:
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges. CoRR abs/2407.09618 (2024) - [i176]Veronica Chelu, Doina Precup:
Functional Acceleration for Policy Mirror Descent. CoRR abs/2407.16602 (2024) - [i175]Chenqing Hua, Bozitao Zhong, Sitao Luan, Liang Hong, Guy Wolf, Doina Precup, Shuangjia Zheng:
Reactzyme: A Benchmark for Enzyme-Reaction Prediction. CoRR abs/2408.13659 (2024) - [i174]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 M. P. Behbahani, Aleksandra Faust:
Training Language Models to Self-Correct via Reinforcement Learning. CoRR abs/2409.12917 (2024) - [i173]Chenqing Hua, Yong Liu, Dinghuai Zhang, Odin Zhang, Sitao Luan, Kevin K. Yang, Guy Wolf, Doina Precup, Shuangjia Zheng:
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics. CoRR abs/2410.00327 (2024) - [i172]Keyu Wang, Abdullah Norozi Iranzad, Scott Schaffter, Doina Precup, Jonathan Lebensold:
Mitigating Downstream Model Risks via Model Provenance. CoRR abs/2410.02230 (2024) - [i171]Mingde Zhao, Tristan Sylvain, Doina Precup, Yoshua Bengio:
Identifying and Addressing Delusions for Target-Directed Decision-Making. CoRR abs/2410.07096 (2024) - [i170]Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards, Doina Precup:
Learning Successor Features the Simple Way. CoRR abs/2410.22133 (2024) - [i169]Marc Lanctot, Kate Larson, Michael Kaisers, Quentin Berthet, Ian Gemp, Manfred Diaz, Roberto-Rafael Maura-Rivero, Yoram Bachrach, Anna Koop, Doina Precup:
Soft Condorcet Optimization for Ranking of General Agents. CoRR abs/2411.00119 (2024) - [i168]Chenqing Hua, Jiarui Lu, Yong Liu, Odin Zhang, Jian Tang, Rex Ying, Wengong Jin, Guy Wolf, Doina Precup, Shuangjia Zheng:
Reaction-conditioned De Novo Enzyme Design with GENzyme. CoRR abs/2411.16694 (2024) - [i167]Wesley Chung, Lynn Cherif, David Meger, Doina Precup:
Parseval Regularization for Continual Reinforcement Learning. CoRR abs/2412.07224 (2024) - [i166]Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro:
MaestroMotif: Skill Design from Artificial Intelligence Feedback. CoRR abs/2412.08542 (2024) - [i165]Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto, Doina Precup, David Meger:
Fairness in Reinforcement Learning with Bisimulation Metrics. CoRR abs/2412.17123 (2024) - 2023
- [j39]Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi, Alexander Wong, Doina Precup:
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning. IEEE Access 11: 42796-42808 (2023) - [j38]Marlos C. Machado, André Barreto, Doina Precup, Michael Bowling:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. J. Mach. Learn. Res. 24: 80:1-80:69 (2023) - [c228]Sumana Basu, Marc-André Legault, Adriana Romero-Soriano, Doina Precup:
On the Challenges of Using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects. AAAI 2023: 14102-14109 - [c227]Flemming Kondrup, Thomas Jiralerspong, Elaine Lau, Nathan de Lara, Jacob Shkrob, My Duc Tran, Doina Precup, Sumana Basu:
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning. AAAI 2023: 15696-15702 - [c226]Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup:
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. AISTATS 2023: 5438-5448 - [c225]Derek Kweku Degbedzui, Michael Kuzniewicz, Marie-Coralie Cornet, Yvonne Wu, Heather Forquer, Lawrence Gerstley, Emily F. Hamilton, Doina Precup, Philip A. Warrick, Robert E. Kearney:
Hybrid Scattering Transform - Long Short-Term Memory Networks for Intrapartum Fetal Heart Rate Classification. CinC 2023: 1-4 - [c224]Safa Alver, Doina Precup:
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning. CoLLAs 2023: 548-567 - [c223]Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Doina Precup:
When Do We Need Graph Neural Networks for Node Classification? COMPLEX NETWORKS (1) 2023: 37-48 - [c222]Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks. COMPLEX NETWORKS (1) 2023: 49-60 - [c221]David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. ICML 2023: 35024-35036 - [c220]Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup:
MUDiff: Unified Diffusion for Complete Molecule Generation. LoG 2023: 33 - [c219]David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh:
A Definition of Continual Reinforcement Learning. NeurIPS 2023 - [c218]Nishanth Anand, Doina Precup:
Prediction and Control in Continual Reinforcement Learning. NeurIPS 2023 - [c217]Scott Fujimoto, Wei-Di Chang, Edward J. Smith, Shixiang Gu, Doina Precup, David Meger:
For SALE: State-Action Representation Learning for Deep Reinforcement Learning. NeurIPS 2023 - [c216]Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup:
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability. NeurIPS 2023 - [e4]Sarath Chandar, Razvan Pascanu, Hanie Sedghi, Doina Precup:
Conference on Lifelong Learning Agents, 22-25 August 2023, McGill University, Montréal, Québec, Canada. Proceedings of Machine Learning Research 232, PMLR 2023 [contents] - [i164]Sumana Basu, Marc-André Legault, Adriana Romero-Soriano, Doina Precup:
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects. CoRR abs/2301.00512 (2023) - [i163]Safa Alver, Doina Precup:
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning. CoRR abs/2301.10119 (2023) - [i162]Kushal Arora, Timothy J. O'Donnell, Doina Precup, Jason Weston, Jackie Chi Kit Cheung:
The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation. CoRR abs/2302.06784 (2023) - [i161]Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, Ankit Anand:
Accelerating exploration and representation learning with offline pre-training. CoRR abs/2304.00046 (2023) - [i160]Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup:
When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability. CoRR abs/2304.14274 (2023) - [i159]Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup:
MUDiff: Unified Diffusion for Complete Molecule Generation. CoRR abs/2304.14621 (2023) - [i158]Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup:
Policy Gradient Methods in the Presence of Symmetries and State Abstractions. CoRR abs/2305.05666 (2023) - [i157]Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli:
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. CoRR abs/2305.18246 (2023) - [i156]Scott Fujimoto, Wei-Di Chang, Edward J. Smith, Shixiang Shane Gu, Doina Precup, David Meger:
For SALE: State-Action Representation Learning for Deep Reinforcement Learning. CoRR abs/2306.02451 (2023) - [i155]Veronica Chelu, Tom Zahavy, Arthur Guez, Doina Precup, Sebastian Flennerhag:
Optimism and Adaptivity in Policy Optimization. CoRR abs/2306.10587 (2023) - [i154]Nikhil Vemgal, Elaine Lau, Doina Precup:
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets. CoRR abs/2307.07674 (2023) - [i153]David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh:
On the Convergence of Bounded Agents. CoRR abs/2307.11044 (2023) - [i152]David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh:
A Definition of Continual Reinforcement Learning. CoRR abs/2307.11046 (2023) - [i151]Shruti Mishra, Ankit Anand, Jordan Hoffmann, Nicolas Heess, Martin A. Riedmiller, Abbas Abdolmaleki, Doina Precup:
Policy composition in reinforcement learning via multi-objective policy optimization. CoRR abs/2308.15470 (2023) - [i150]Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio:
Combining Spatial and Temporal Abstraction in Planning for Better Generalization. CoRR abs/2310.00229 (2023) - [i149]Charles C. Onu, Samantha Latremouille, Arsenii Gorin, Junhao Wang, Uchenna Ekwochi, Peter O. Ubuane, Omolara A. Kehinde, Muhammad A. Salisu, Datonye Briggs, Yoshua Bengio, Doina Precup:
A cry for help: Early detection of brain injury in newborns. CoRR abs/2310.08338 (2023) - [i148]Thomas Jiralerspong, Flemming Kondrup, Doina Precup, Khimya Khetarpal:
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels. CoRR abs/2310.09997 (2023) - [i147]Elaine Lau, Nikhil Vemgal, Doina Precup, Emmanuel Bengio:
DGFN: Double Generative Flow Networks. CoRR abs/2310.19685 (2023) - [i146]Jonathan Colaço Carr, Prakash Panangaden, Doina Precup:
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies. CoRR abs/2311.01990 (2023) - [i145]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:
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search. CoRR abs/2311.03583 (2023) - [i144]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. CoRR abs/2312.00886 (2023) - [i143]Nishanth Anand, Doina Precup:
Prediction and Control in Continual Reinforcement Learning. CoRR abs/2312.11669 (2023) - 2022
- [j37]Faizy Ahsan, Zichao Yan, Doina Precup, Mathieu Blanchette:
PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information. Bioinform. 38(Supplement_1): i299-i306 (2022) - [j36]Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau:
Low-Rank Representation of Reinforcement Learning Policies. J. Artif. Intell. Res. 75: 597-636 (2022) - [j35]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. J. Artif. Intell. Res. 75: 1401-1476 (2022) - [j34]Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto:
Deep learning, reinforcement learning, and world models. Neural Networks 152: 267-275 (2022) - [j33]Leo Schwinn, Doina Precup, Björn M. Eskofier, Dario Zanca:
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention. Trans. Mach. Learn. Res. 2022 (2022) - [c215]Derek Kweku Degbedzui, Michael Kuzniewicz, Marie-Coralie Cornet, Yvonne Wu, Heather Forquer, Lawrence Gerstley, Emily F. Hamilton, Doina Precup, Philip A. Warrick, Robert E. Kearney:
Assessing Intrapartum Risk of Hypoxic Ischemic Encephalopathy Using Fetal Heart Rate With Long Short-Term Memory Networks. CinC 2022: 1-4 - [c214]Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez:
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. ICLR 2022 - [c213]Safa Alver, Doina Precup:
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates. ICLR 2022 - [c212]David Venuto, Elaine Lau, Doina Precup, Ofir Nachum:
Policy Gradients Incorporating the Future. ICLR 2022 - [c211]Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen Marcus McAleer, Vlad Firoiu, Lei M. Zhang, Doina Precup, Shibl Mourad:
Proving Theorems using Incremental Learning and Hindsight Experience Replay. ICML 2022: 1198-1210 - [c210]Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu:
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. ICML 2022: 6918-6943 - [c209]Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern M. Eskofier, Dario Zanca:
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. ICML 2022: 19434-19449 - [c208]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward (Extended Abstract). IJCAI 2022: 5254-5258 - [c207]Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup:
Revisiting Heterophily For Graph Neural Networks. NeurIPS 2022 - [c206]Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup:
Continuous MDP Homomorphisms and Homomorphic Policy Gradient. NeurIPS 2022 - [c205]Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesváari, Doina Precup:
Towards painless policy optimization for constrained MDPs. UAI 2022: 895-905 - [e3]Sarath Chandar, Razvan Pascanu, Doina Precup:
Conference on Lifelong Learning Agents, CoLLAs 2022, 22-24 August 2022, McGill University, Montréal, Québec, Canada. Proceedings of Machine Learning Research 199, PMLR 2022 [contents] - [i142]Raviteja Chunduru, Doina Precup:
Attention Option-Critic. CoRR abs/2201.02628 (2022) - [i141]Andrei Cristian Nica, Khimya Khetarpal, Doina Precup:
The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning. CoRR abs/2201.09653 (2022) - [i140]Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu:
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. CoRR abs/2201.12417 (2022) - [i139]Amir Ardalan Kalantari, Mohammad Amini, Sarath Chandar, Doina Precup:
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers. CoRR abs/2202.00710 (2022) - [i138]Veronica Chelu, Diana Borsa, Doina Precup, Hado van Hasselt:
Selective Credit Assignment. CoRR abs/2202.09699 (2022) - [i137]Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup:
Towards Painless Policy Optimization for Constrained MDPs. CoRR abs/2204.05176 (2022) - [i136]Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez:
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. CoRR abs/2204.08957 (2022) - [i135]Leo Schwinn, Doina Precup, Björn M. Eskofier, Dario Zanca:
Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention. CoRR abs/2204.09093 (2022) - [i134]Gheorghe Comanici, Amelia Glaese, Anita Gergely, Daniel Toyama, Zafarali Ahmed, Tyler Jackson, Philippe Hamel, Doina Precup:
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning. CoRR abs/2204.10374 (2022) - [i133]Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Björn M. Eskofier, Dario Zanca:
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. CoRR abs/2205.09619 (2022) - [i132]Safa Alver, Doina Precup:
Understanding Decision-Time vs. Background Planning in Model-Based Reinforcement Learning. CoRR abs/2206.08442 (2022) - [i131]Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden, David Meger, Doina Precup:
Continuous MDP Homomorphisms and Homomorphic Policy Gradient. CoRR abs/2209.07364 (2022) - [i130]Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek:
Bayesian Q-learning With Imperfect Expert Demonstrations. CoRR abs/2210.01800 (2022) - [i129]Flemming Kondrup, Thomas Jiralerspong, Elaine Lau, Nathan de Lara, Jacob Shkrob, My Duc Tran, Doina Precup, Sumana Basu:
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning. CoRR abs/2210.02552 (2022) - [i128]Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj, Doina Precup:
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation. CoRR abs/2210.05918 (2022) - [i127]Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup:
Revisiting Heterophily For Graph Neural Networks. CoRR abs/2210.07606 (2022) - [i126]Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Doina Precup:
When Do We Need GNN for Node Classification? CoRR abs/2210.16979 (2022) - [i125]Gandharv Patil, Aditya Mahajan, Doina Precup:
On learning history based policies for controlling Markov decision processes. CoRR abs/2211.03011 (2022) - [i124]Leo Schwinn, Doina Precup, Björn M. Eskofier, Dario Zanca:
Simulating Human Gaze with Neural Visual Attention. CoRR abs/2211.12100 (2022) - [i123]David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum:
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. CoRR abs/2211.13337 (2022) - [i122]Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup:
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks. CoRR abs/2212.10822 (2022) - [i121]Riashat Islam, Samarth Sinha, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Animesh Garg, Zhaoran Wang, Lihong Li, Doina Precup:
Offline Policy Optimization in RL with Variance Regularizaton. CoRR abs/2212.14405 (2022) - 2021
- [j32]David Silver, Satinder Singh, Doina Precup, Richard S. Sutton:
Reward is enough. Artif. Intell. 299: 103535 (2021) - [j31]Arushi Jain, Khimya Khetarpal, Doina Precup:
Safe option-critic: learning safety in the option-critic architecture. Knowl. Eng. Rev. 36: e4 (2021) - [c204]Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup:
Variance Penalized On-Policy and Off-Policy Actor-Critic. AAAI 2021: 7899-7907 - [c203]Haiping Wu, Khimya Khetarpal, Doina Precup:
Self-Supervised Attention-Aware Reinforcement Learning. AAAI 2021: 10311-10319 - [c202]Borja Balle, Clara Lacroce, Prakash Panangaden, Doina Precup, Guillaume Rabusseau:
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. ICALP 2021: 118:1-118:20 - [c201]Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup:
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards. ICML 2021: 275-285 - [c200]Nishanth V. Anand, Doina Precup:
Preferential Temporal Difference Learning. ICML 2021: 286-296 - [c199]Scott Fujimoto, David Meger, Doina Precup:
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. ICML 2021: 3518-3529 - [c198]Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang:
Randomized Exploration in Reinforcement Learning with General Value Function Approximation. ICML 2021: 4607-4616 - [c197]Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. NeurIPS 2021: 1256-1272 - [c196]Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio:
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. NeurIPS 2021: 1569-1581 - [c195]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup:
Temporally Abstract Partial Models. NeurIPS 2021: 1979-1991 - [c194]Martin Klissarov, Doina Precup:
Flexible Option Learning. NeurIPS 2021: 4632-4646 - [c193]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. NeurIPS 2021: 7799-7812 - [c192]Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio:
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. NeurIPS 2021: 27381-27394 - [i120]Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup:
Variance Penalized On-Policy and Off-Policy Actor-Critic. CoRR abs/2102.01985 (2021) - [i119]Borja Balle, Clara Lacroce, Prakash Panangaden, Doina Precup, Guillaume Rabusseau:
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata. CoRR abs/2102.06860 (2021) - [i118]Vlad Firoiu, Eser Aygün, Ankit Anand, Zafarali Ahmed, Xavier Glorot, Laurent Orseau, Lei M. Zhang, Doina Precup, Shibl Mourad:
Training a First-Order Theorem Prover from Synthetic Data. CoRR abs/2103.03798 (2021) - [i117]Safa Alver, Doina Precup:
What is Going on Inside Recurrent Meta Reinforcement Learning Agents? CoRR abs/2104.14644 (2021) - [i116]Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, Doina Precup:
AndroidEnv: A Reinforcement Learning Platform for Android. CoRR abs/2105.13231 (2021) - [i115]Bogdan Mazoure, Paul Mineiro, Pavithra Srinath, Reza Sharifi Sedeh, Doina Precup, Adith Swaminathan:
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL. CoRR abs/2106.00589 (2021) - [i114]Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio:
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. CoRR abs/2106.02097 (2021) - [i113]Emmanuel Bengio, Joelle Pineau, Doina Precup:
Correcting Momentum in Temporal Difference Learning. CoRR abs/2106.03955 (2021) - [i112]Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio:
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. CoRR abs/2106.04399 (2021) - [i111]Nishanth Anand, Doina Precup:
Preferential Temporal Difference Learning. CoRR abs/2106.06508 (2021) - [i110]Scott Fujimoto, David Meger, Doina Precup:
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. CoRR abs/2106.06854 (2021) - [i109]Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin F. Yang:
Randomized Exploration for Reinforcement Learning with General Value Function Approximation. CoRR abs/2106.07841 (2021) - [i108]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. CoRR abs/2106.13105 (2021) - [i107]David Venuto, Elaine Lau, Doina Precup, Ofir Nachum:
Policy Gradients Incorporating the Future. CoRR abs/2108.02096 (2021) - [i106]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup:
Temporally Abstract Partial Models. CoRR abs/2108.03213 (2021) - [i105]Susan Amin, Maziar Gomrokchi, Harsh Satija, Herke van Hoof, Doina Precup:
A Survey of Exploration Methods in Reinforcement Learning. CoRR abs/2109.00157 (2021) - [i104]Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi, Alexander Wong, Doina Precup:
Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning. CoRR abs/2109.03975 (2021) - [i103]Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup:
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? CoRR abs/2109.05641 (2021) - [i102]Marlos C. Machado, André Barreto, Doina Precup:
Temporal Abstraction in Reinforcement Learning with the Successor Representation. CoRR abs/2110.05740 (2021) - [i101]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. CoRR abs/2111.00876 (2021) - [i100]Martin Klissarov, Doina Precup:
Flexible Option Learning. CoRR abs/2112.03097 (2021) - [i99]Eser Aygün, Laurent Orseau, Ankit Anand, Xavier Glorot, Vlad Firoiu, Lei M. Zhang, Doina Precup, Shibl Mourad:
Proving Theorems using Incremental Learning and Hindsight Experience Replay. CoRR abs/2112.10664 (2021) - [i98]Safa Alver, Doina Precup:
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates. CoRR abs/2112.15025 (2021) - [i97]Samin Yeasar Arnob, Riashat Islam, Doina Precup:
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning. CoRR abs/2112.15578 (2021) - [i96]Samin Yeasar Arnob, Riyasat Ohib, Sergey M. Plis, Doina Precup:
Single-Shot Pruning for Offline Reinforcement Learning. CoRR abs/2112.15579 (2021) - 2020
- [j30]Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel:
Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Medical Image Anal. 59 (2020) - [j29]André Barreto, Shaobo Hou, Diana Borsa, David Silver, Doina Precup:
Fast reinforcement learning with generalized policy updates. Proc. Natl. Acad. Sci. USA 117(48): 30079-30087 (2020) - [j28]Di Wu, Boyu Wang, Doina Precup, Benoit Boulet:
Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting. IEEE Trans. Smart Grid 11(2): 1183-1192 (2020) - [c191]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. AAAI 2020: 4328-4336 - [c190]Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup:
Options of Interest: Temporal Abstraction with Interest Functions. AAAI 2020: 4444-4451 - [c189]Andrei Lupu, Doina Precup:
Gifting in Multi-Agent Reinforcement Learning (Student Abstract). AAAI 2020: 13871-13872 - [c188]David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman:
Value Preserving State-Action Abstractions. AISTATS 2020: 1639-1650 - [c187]Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. AISTATS 2020: 2852-2862 - [c186]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. AISTATS 2020: 4357-4366 - [c185]Andrei Lupu, Doina Precup:
Gifting in Multi-Agent Reinforcement Learning. AAMAS 2020: 789-797 - [c184]Mingde Zhao, Sitao Luan, Ian Porada, Xiao-Wen Chang, Doina Precup:
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation. AAMAS 2020: 1647-1655 - [c183]Jhelum Chakravorty, Patrick Nadeem Ward, Julien Roy, Maxime Chevalier-Boisvert, Sumana Basu, Andrei Lupu, Doina Precup:
Option-Critic in Cooperative Multi-agent Systems. AAMAS 2020: 1792-1794 - [c182]Faizy Ahsan, Alexandre Drouin, François Laviolette, Doina Precup, Mathieu Blanchette:
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites. BIBM 2020: 62-66 - [c181]Ivana Kajic, Eser Aygün, Doina Precup:
Learning to cooperate: Emergent communication in multi-agent navigation. CogSci 2020 - [c180]Doina Precup:
Keynote Lecture - Building Knowledge For AI AgentsWith Reinforcement Learning. ICCP 2020: 1 - [c179]Emmanuel Bengio, Joelle Pineau, Doina Precup:
Interference and Generalization in Temporal Difference Learning. ICML 2020: 767-777 - [c178]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. ICML 2020: 5243-5253 - [c177]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. ICML 2020: 11214-11224 - [c176]Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup:
SVRG for Policy Evaluation with Fewer Gradient Evaluations. IJCAI 2020: 2697-2703 - [c175]Veronica Chelu, Doina Precup, Hado van Hasselt:
Forethought and Hindsight in Credit Assignment. NeurIPS 2020 - [c174]Scott Fujimoto, David Meger, Doina Precup:
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. NeurIPS 2020 - [c173]Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. NeurIPS 2020 - [c172]Martin Klissarov, Doina Precup:
Reward Propagation Using Graph Convolutional Networks. NeurIPS 2020 - [c171]Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh:
On Efficiency in Hierarchical Reinforcement Learning. NeurIPS 2020 - [i95]Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup:
Options of Interest: Temporal Abstraction with Interest Functions. CoRR abs/2001.00271 (2020) - [i94]Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau:
Provably efficient reconstruction of policy networks. CoRR abs/2002.02863 (2020) - [i93]Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. CoRR abs/2002.08329 (2020) - [i92]David Venuto, Jhelum Chakravorty, Léonard Boussioux, Junhao Wang, Gavin McCracken, Doina Precup:
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions. CoRR abs/2002.09043 (2020) - [i91]Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon:
Policy Evaluation Networks. CoRR abs/2002.11833 (2020) - [i90]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. CoRR abs/2003.06016 (2020) - [i89]Emmanuel Bengio, Joelle Pineau, Doina Precup:
Interference and Generalization in Temporal Difference Learning. CoRR abs/2003.06350 (2020) - [i88]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. CoRR abs/2003.12239 (2020) - [i87]Ivana Kajic, Eser Aygün, Doina Precup:
Learning to cooperate: Emergent communication in multi-agent navigation. CoRR abs/2004.01097 (2020) - [i86]Safa Alver, Doina Precup:
A Brief Look at Generalization in Visual Meta-Reinforcement Learning. CoRR abs/2006.07262 (2020) - [i85]Eser Aygün, Zafarali Ahmed, Ankit Anand, Vlad Firoiu, Xavier Glorot, Laurent Orseau, Doina Precup, Shibl Mourad:
Learning to Prove from Synthetic Theorems. CoRR abs/2006.11259 (2020) - [i84]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. CoRR abs/2006.15085 (2020) - [i83]Scott Fujimoto, David Meger, Doina Precup:
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. CoRR abs/2007.06049 (2020) - [i82]Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks. CoRR abs/2008.08838 (2020) - [i81]Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup:
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks. CoRR abs/2008.08844 (2020) - [i80]Martin Klissarov, Doina Precup:
Reward Propagation Using Graph Convolutional Networks. CoRR abs/2010.02474 (2020) - [i79]Charles C. Onu, Jacob E. Miller, Doina Precup:
A Fully Tensorized Recurrent Neural Network. CoRR abs/2010.04196 (2020) - [i78]Tianyu Li, Doina Precup, Guillaume Rabusseau:
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning. CoRR abs/2010.10029 (2020) - [i77]Veronica Chelu, Doina Precup, Hado van Hasselt:
Forethought and Hindsight in Credit Assignment. CoRR abs/2010.13685 (2020) - [i76]Gavin McCracken, Colin Daniels, Rosie Zhao, Anna M. Brandenberger, Prakash Panangaden, Doina Precup:
A Study of Policy Gradient on a Class of Exactly Solvable Models. CoRR abs/2011.01859 (2020) - [i75]Anand Kamat, Doina Precup:
Diversity-Enriched Option-Critic. CoRR abs/2011.02565 (2020) - [i74]Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie:
Gradient Starvation: A Learning Proclivity in Neural Networks. CoRR abs/2011.09468 (2020) - [i73]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. CoRR abs/2012.13490 (2020) - [i72]Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup:
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards. CoRR abs/2012.13658 (2020)
2010 – 2019
- 2019
- [j27]Borja Balle, Prakash Panangaden, Doina Precup:
Singular value automata and approximate minimization. Math. Struct. Comput. Sci. 29(9): 1444-1478 (2019) - [c170]Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup:
Temporally Extended Metrics for Markov Decision Processes. SafeAI@AAAI 2019 - [c169]Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau:
Combined Reinforcement Learning via Abstract Representations. AAAI 2019: 3582-3589 - [c168]Andrei Lupu, Audrey Durand, Doina Precup:
Leveraging Observations in Bandits: Between Risks and Benefits. AAAI 2019: 6112-6119 - [c167]Khimya Khetarpal, Doina Precup:
Learning Options with Interest Functions. AAAI 2019: 9955-9956 - [c166]Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. AISTATS 2019: 1630-1639 - [c165]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. AISTATS 2019: 2231-2240 - [c164]Doina Precup:
Building Knowledge for AI Agents with Reinforcement Learning. AAMAS 2019: 6 - [c163]Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira Ebrahimi Kahou, Joseph Paul Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal:
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments. CoRL 2019: 1314-1327 - [c162]Zafarali Ahmed, Arjun Karuvally, Doina Precup, Simon Gravel:
Learning proposals for sequential importance samplers using reinforced variational inference. DeepRLStructPred@ICLR 2019 - [c161]Scott Fujimoto, David Meger, Doina Precup:
Off-Policy Deep Reinforcement Learning without Exploration. ICML 2019: 2052-2062 - [c160]Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup:
Per-Decision Option Discounting. ICML 2019: 2644-2652 - [c159]Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger:
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. ICRA 2019: 768-774 - [c158]Hossein Aboutalebi, Doina Precup, Tibor Schuster:
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials. AISafety@IJCAI 2019 - [c157]Hossein Aboutalebi, Doina Precup, Tibor Schuster:
Learning Reliable Policies in the Bandit Setting with Application to Adaptive Clinical Trials. KDH@IJCAI 2019: 43-49 - [c156]Charles C. Onu, Jonathan Lebensold, William L. Hamilton, Doina Precup:
Neural Transfer Learning for Cry-Based Diagnosis of Perinatal Asphyxia. INTERSPEECH 2019: 3053-3057 - [c155]Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold, Tal Arbel:
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases. DART/MIL3ID@MICCAI 2019: 90-98 - [c154]Sumana Basu, Konrad Wagstyl, Azar Zandifar, D. Louis Collins, Adriana Romero, Doina Precup:
Early Prediction of Alzheimer's Disease Progression Using Variational Autoencoders. MICCAI (4) 2019: 205-213 - [c153]Adrian Tousignant, Paul Lemaître, Doina Precup, Douglas L. Arnold, Tal Arbel:
Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data. MIDL 2019: 483-492 - [c152]Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. NeurIPS 2019: 10943-10953 - [c151]Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos:
Hindsight Credit Assignment. NeurIPS 2019: 12467-12476 - [c150]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. NeurIPS 2019: 13031-13041 - [i71]Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad, Charles Blundell, Doina Precup:
Community size effect in artificial learning systems. ViGIL@NeurIPS 2019 - [i70]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. CoRR abs/1902.09996 (2019) - [i69]Hossein Aboutalebi, Doina Precup, Tibor Schuster:
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials. CoRR abs/1903.01026 (2019) - [i68]Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger:
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. CoRR abs/1903.05697 (2019) - [i67]Pierre Thodoroff, Nishanth Anand, Lucas Caccia, Doina Precup, Joelle Pineau:
Recurrent Value Functions. CoRR abs/1905.09562 (2019) - [i66]Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup:
SVRG for Policy Evaluation with Fewer Gradient Evaluations. CoRR abs/1906.03704 (2019) - [i65]Charles C. Onu, Jonathan Lebensold, William L. Hamilton, Doina Precup:
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia. CoRR abs/1906.10199 (2019) - [i64]Srinivas Venkattaramanujam, Eric Crawford, Thang Doan, Doina Precup:
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning. CoRR abs/1907.02998 (2019) - [i63]Vincent Michalski, Vikram Voleti, Samira Ebrahimi Kahou, Anthony Ortiz, Pascal Vincent, Chris Pal, Doina Precup:
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation. CoRR abs/1908.00061 (2019) - [i62]Sitao Luan, Xiao-Wen Chang, Doina Precup:
Revisit Policy Optimization in Matrix Form. CoRR abs/1909.09186 (2019) - [i61]David Venuto, Léonard Boussioux, Junhao Wang, Rola Dali, Jhelum Chakravorty, Yoshua Bengio, Doina Precup:
Avoidance Learning Using Observational Reinforcement Learning. CoRR abs/1909.11228 (2019) - [i60]Shruti Mishra, Abbas Abdolmaleki, Arthur Guez, Piotr Trochim, Doina Precup:
Augmenting learning using symmetry in a biologically-inspired domain. CoRR abs/1910.00528 (2019) - [i59]Jonathan Lebensold, William L. Hamilton, Borja Balle, Doina Precup:
Actor Critic with Differentially Private Critic. CoRR abs/1910.05876 (2019) - [i58]Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira Ebrahimi Kahou, Joseph Paul Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal:
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments. CoRR abs/1910.13249 (2019) - [i57]Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau:
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. CoRR abs/1911.05010 (2019) - [i56]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction. CoRR abs/1911.12511 (2019) - [i55]Jhelum Chakravorty, Patrick Nadeem Ward, Julien Roy, Maxime Chevalier-Boisvert, Sumana Basu, Andrei Lupu, Doina Precup:
Option-critic in cooperative multi-agent systems. CoRR abs/1911.12825 (2019) - [i54]Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos:
Hindsight Credit Assignment. CoRR abs/1912.02503 (2019) - [i53]Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup:
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods. CoRR abs/1912.05104 (2019) - [i52]Riashat Islam, Raihan Seraj, Samin Yeasar Arnob, Doina Precup:
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning. CoRR abs/1912.05109 (2019) - [i51]Riashat Islam, Zafarali Ahmed, Doina Precup:
Marginalized State Distribution Entropy Regularization in Policy Optimization. CoRR abs/1912.05128 (2019) - [i50]Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad, Charles Blundell, Doina Precup:
Shaping representations through communication: community size effect in artificial learning systems. CoRR abs/1912.06208 (2019) - 2018
- [j26]Pierre-Luc Bacon, Doina Precup:
Constructing Temporal Abstractions Autonomously in Reinforcement Learning. AI Mag. 39(1): 39-50 (2018) - [c149]Yuri Grinberg, Hossein Aboutalebi, Melanie Lyman-Abramovitch, Borja Balle, Doina Precup:
Learning Predictive State Representations From Non-Uniform Sampling. AAAI 2018: 3061-3068 - [c148]Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup:
When Waiting Is Not an Option: Learning Options With a Deliberation Cost. AAAI 2018: 3165-3172 - [c147]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning With Options That Terminate Off-Policy. AAAI 2018: 3173-3182 - [c146]Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup:
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning. AAAI 2018: 3199-3206 - [c145]Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger:
Deep Reinforcement Learning That Matters. AAAI 2018: 3207-3214 - [c144]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. AAAI 2018: 6409-6416 - [c143]Andrei Lupu, Doina Precup:
Imitation Upper Confidence Bound for Bandits on a Graph. AAAI 2018: 8113-8114 - [c142]Tianyu Li, Guillaume Rabusseau, Doina Precup:
Nonlinear Weighted Finite Automata. AISTATS 2018: 679-688 - [c141]Ayush Jain, Doina Precup:
Eligibility Traces for Options. AAMAS 2018: 1008-1016 - [c140]Andrei Lupu, Audrey Durand, Doina Precup:
Leveraging Observational Learning for Exploration in Bandits. AAMAS 2018: 2001-2003 - [c139]Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Doina Precup, Robert E. Kearney:
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants. EMBC 2018: 4940-4944 - [c138]Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent:
Convergent TREE BACKUP and RETRACE with Function Approximation. ICML 2018: 4962-4971 - [c137]Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel:
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation. MICCAI (1) 2018: 655-663 - [c136]Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup:
Temporal Regularization for Markov Decision Process. NeurIPS 2018: 1784-1794 - [c135]Jessie Huang, Fa Wu, Doina Precup, Yang Cai:
Learning Safe Policies with Expert Guidance. NeurIPS 2018: 9123-9132 - [c134]Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup:
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization. *SEM@NAACL-HLT 2018: 1-10 - [i49]Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor:
Learning Robust Options. CoRR abs/1802.03236 (2018) - [i48]Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, Yoshua Bengio:
Disentangling the independently controllable factors of variation by interacting with the world. CoRR abs/1802.09484 (2018) - [i47]Jessie Huang, Fa Wu, Doina Precup, Yang Cai:
Learning Safe Policies with Expert Guidance. CoRR abs/1805.08313 (2018) - [i46]Ryan Faulkner, Doina Precup:
Dyna Planning using a Feature Based Generative Model. CoRR abs/1805.10129 (2018) - [i45]Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup:
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization. CoRR abs/1805.10985 (2018) - [i44]Guillaume Rabusseau, Tianyu Li, Doina Precup:
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning. CoRR abs/1807.01406 (2018) - [i43]Arushi Jain, Khimya Khetarpal, Doina Precup:
Safe Option-Critic: Learning Safety in the Option-Critic Architecture. CoRR abs/1807.08060 (2018) - [i42]Khimya Khetarpal, Doina Precup:
Attend Before you Act: Leveraging human visual attention for continual learning. CoRR abs/1807.09664 (2018) - [i41]Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel:
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation. CoRR abs/1808.01200 (2018) - [i40]Charles C. Onu, Lara J. Kanbar, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing. CoRR abs/1808.07991 (2018) - [i39]Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants. CoRR abs/1808.07992 (2018) - [i38]Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau:
Combined Reinforcement Learning via Abstract Representations. CoRR abs/1809.04506 (2018) - [i37]Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup:
Temporal Regularization in Markov Decision Process. CoRR abs/1811.00429 (2018) - [i36]Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup:
The Barbados 2018 List of Open Issues in Continual Learning. CoRR abs/1811.07004 (2018) - [i35]Khimya Khetarpal, Shagun Sodhani, Sarath Chandar, Doina Precup:
Environments for Lifelong Reinforcement Learning. CoRR abs/1811.10732 (2018) - [i34]Scott Fujimoto, David Meger, Doina Precup:
Off-Policy Deep Reinforcement Learning without Exploration. CoRR abs/1812.02900 (2018) - [i33]Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia, Guillaume Rabusseau, Jackie Chi Kit Cheung, Doina Precup:
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss. CoRR abs/1812.07627 (2018) - 2017
- [c133]Pierre-Luc Bacon, Jean Harb, Doina Precup:
The Option-Critic Architecture. AAAI 2017: 1726-1734 - [c132]Negar Ghourchian, Michel Allegue-Martínez, Doina Precup:
Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning. AAAI 2017: 4670-4677 - [c131]Charles C. Onu, Lara J. Kanbar, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
A semi-Markov chain approach to modeling respiratory patterns prior to extubation in preterm infants. EMBC 2017: 2022-2026 - [c130]Lara J. Kanbar, Wissam Shalish, Doina Precup, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney:
APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies. EMBC 2017: 2602-2605 - [c129]Teng Long, Emmanuel Bengio, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup:
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions. EMNLP 2017: 825-834 - [c128]Jesús Alejandro Cárdenes Cabré, Doina Precup, Ricardo Sanz:
Horizontal and Vertical Self-Adaptive Cloud Controller with Reward Optimization for Resource Allocation. ICCAC 2017: 184-185 - [c127]Timothy A. Mann, Shie Mannor, Doina Precup:
Approximate Value Iteration with Temporally Extended Actions (Extended Abstract). IJCAI 2017: 5035-5039 - [c126]Andrew Doyle, Doina Precup, Douglas L. Arnold, Tal Arbel:
Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation. MICCAI (3) 2017: 186-194 - [c125]Sharmin Nilufar, Dao Sen Wang, John Girgis, Carmen G. Palii, D. Yang, A. Blais, M. Brand, Doina Precup, Theodore J. Perkins:
Learning-based interactive segmentation using the maximum mean cycle weight formalism. Image Processing 2017: 101332S - [c124]Di Wu, Boyu Wang, Doina Precup, Benoit Boulet:
Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting. ECML/PKDD (3) 2017: 39-51 - [c123]Charles C. Onu, Lara J. Kanbar, Wissam Shalish, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
Predicting extubation readiness in extreme preterm infants based on patterns of breathing. SSCI 2017: 1-7 - [e2]Doina Precup, Yee Whye Teh:
Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017. Proceedings of Machine Learning Research 70, PMLR 2017 [contents] - [i32]Peeyush Kumar, Doina Precup:
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options. CoRR abs/1703.06471 (2017) - [i31]Emmanuel Bengio, Valentin Thomas, Joelle Pineau, Doina Precup, Yoshua Bengio:
Independently Controllable Features. CoRR abs/1703.07718 (2017) - [i30]Jean Harb, Doina Precup:
Investigating Recurrence and Eligibility Traces in Deep Q-Networks. CoRR abs/1704.05495 (2017) - [i29]Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent:
Convergent Tree-Backup and Retrace with Function Approximation. CoRR abs/1705.09322 (2017) - [i28]Philip Bachman, Doina Precup:
Variational Generative Stochastic Networks with Collaborative Shaping. CoRR abs/1708.00805 (2017) - [i27]Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, Yoshua Bengio:
Independently Controllable Factors. CoRR abs/1708.01289 (2017) - [i26]Riashat Islam, Peter Henderson, Maziar Gomrokchi, Doina Precup:
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control. CoRR abs/1708.04133 (2017) - [i25]Tianyu Li, Guillaume Rabusseau, Doina Precup:
Neural Network Based Nonlinear Weighted Finite Automata. CoRR abs/1709.04380 (2017) - [i24]Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup:
When Waiting is not an Option : Learning Options with a Deliberation Cost. CoRR abs/1709.04571 (2017) - [i23]Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger:
Deep Reinforcement Learning that Matters. CoRR abs/1709.06560 (2017) - [i22]Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup:
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning. CoRR abs/1709.06683 (2017) - [i21]Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé:
Learning with Options that Terminate Off-Policy. CoRR abs/1711.03817 (2017) - [i20]Borja Balle, Prakash Panangaden, Doina Precup:
Singular value automata and approximate minimization. CoRR abs/1711.05994 (2017) - [i19]Charles C. Onu, Innocent Udeogu, Eyenimi Ndiomu, Urbain Kengni, Doina Precup, Guilherme M. Sant'Anna, Edward Alikor, Peace Opara:
Ubenwa: Cry-based Diagnosis of Birth Asphyxia. CoRR abs/1711.06405 (2017) - [i18]Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup:
Learnings Options End-to-End for Continuous Action Tasks. CoRR abs/1712.00004 (2017) - 2016
- [j25]Tal Arbel, Manuel Jorge Cardoso, William M. Wells III, Albert C. S. Chung, Doina Precup:
Editorial on Special Issue on Probabilistic Models for Biomedical Image Analysis. Comput. Vis. Image Underst. 151: 1-2 (2016) - [j24]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Practical Kernel-Based Reinforcement Learning. J. Mach. Learn. Res. 17: 67:1-67:70 (2016) - [j23]Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel:
Hierarchical Spatio-Temporal Probabilistic Graphical Model with Multiple Feature Fusion for Binary Facial Attribute Classification in Real-World Face Videos. IEEE Trans. Pattern Anal. Mach. Intell. 38(6): 1185-1203 (2016) - [c122]André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup:
Incremental Stochastic Factorization for Online Reinforcement Learning. AAAI 2016: 1468-1475 - [c121]Teng Long, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup:
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data. ACL (2) 2016 - [c120]Faizy Ahsan, Doina Precup, Mathieu Blanchette:
Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy. BCB 2016: 497-498 - [c119]Lara J. Kanbar, Wissam Shalish, Doina Precup, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney:
Automated ongoing data validation and quality control of multi-institutional studies. EMBC 2016: 2504-2507 - [c118]Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup:
Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms. EMNLP 2016: 1734-1743 - [c117]Borja Balle, Maziar Gomrokchi, Doina Precup:
Differentially Private Policy Evaluation. ICML 2016: 2130-2138 - [c116]Lucas Langer, Borja Balle, Doina Precup:
Learning Multi-Step Predictive State Representations. IJCAI 2016: 1662-1668 - [i17]Borja Balle, Maziar Gomrokchi, Doina Precup:
Differentially Private Policy Evaluation. CoRR abs/1603.02010 (2016) - [i16]Teng Long, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup:
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data. CoRR abs/1605.05416 (2016) - [i15]Pierre-Luc Bacon, Jean Harb, Doina Precup:
The Option-Critic Architecture. CoRR abs/1609.05140 (2016) - [i14]Pierre-Luc Bacon, Doina Precup:
A Matrix Splitting Perspective on Planning with Options. CoRR abs/1612.00916 (2016) - 2015
- [j22]Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel:
Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos. Comput. Vis. Image Underst. 136: 128-145 (2015) - [j21]Timothy A. Mann, Shie Mannor, Doina Precup:
Approximate Value Iteration with Temporally Extended Actions. J. Artif. Intell. Res. 53: 375-438 (2015) - [j20]Nastaran Jafarpour, Masoumeh T. Izadi, Doina Precup, David L. Buckeridge:
Quantifying the determinants of outbreak detection performance through simulation and machine learning. J. Biomed. Informatics 53: 180-187 (2015) - [j19]Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh:
Classification-Based Approximate Policy Iteration. IEEE Trans. Autom. Control. 60(11): 2989-2993 (2015) - [j18]Bjoern H. Menze, András Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin S. Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth R. Gerstner, Marc-André Weber, Tal Arbel, Brian B. Avants, Nicholas Ayache, Patricia Buendia, D. Louis Collins, Nicolas Cordier, Jason J. Corso, Antonio Criminisi, Tilak Das, Herve Delingette, Çagatay Demiralp, Christopher R. Durst, Michel Dojat, Senan Doyle, Joana Festa, Florence Forbes, Ezequiel Geremia, Ben Glocker, Polina Golland, Xiaotao Guo, Andac Hamamci, Khan M. Iftekharuddin, Raj Jena, Nigel M. John, Ender Konukoglu, Danial Lashkari, José Antonio Mariz, Raphael Meier, Sérgio Pereira, Doina Precup, Stephen J. Price, Tammy Riklin Raviv, Syed M. S. Reza, Michael T. Ryan, Duygu Sarikaya, Lawrence H. Schwartz, Hoo-Chang Shin, Jamie Shotton, Carlos A. Silva, Nuno J. Sousa, Nagesh K. Subbanna, Gábor Székely, Thomas J. Taylor, Owen M. Thomas, Nicholas J. Tustison, Gözde B. Ünal, Flor Vasseur, Max Wintermark, Dong Hye Ye, Liang Zhao, Binsheng Zhao, Darko Zikic, Marcel Prastawa, Mauricio Reyes, Koen Van Leemput:
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Medical Imaging 34(10): 1993-2024 (2015) - [c115]Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup:
Representation Discovery for MDPs Using Bisimulation Metrics. AAAI 2015: 3578-3584 - [c114]Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup:
Representation Discovery for MDPs Using Bisimulation Metrics. AAAI 2015: 4202-4203 - [c113]Lara J. Kanbar, Wissam Shalish, Carlos A. Robles-Rubio, Doina Precup, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney:
Organizational principles of cloud storage to support collaborative biomedical research. EMBC 2015: 1231-1234 - [c112]Pascale Gourdeau, Lara J. Kanbar, Wissam Shalish, Guilherme M. Sant'Anna, Robert E. Kearney, Doina Precup:
Feature selection and oversampling in analysis of clinical data for extubation readiness in extreme preterm infants. EMBC 2015: 4427-4430 - [c111]Lara J. Kanbar, Wissam Shalish, Carlos A. Robles-Rubio, Doina Precup, Karen A. Brown, Guilherme M. Sant'Anna, Robert E. Kearney:
Correlation of clinical parameters with cardiorespiratory behavior in successfully extubated extremely preterm infants. EMBC 2015: 4431-4434 - [c110]Philip Bachman, Doina Precup:
Variational Generative Stochastic Networks with Collaborative Shaping. ICML 2015: 1964-1972 - [c109]André da Motta Salles Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup:
An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data. IJCAI 2015: 3329-3336 - [c108]Nagesh K. Subbanna, Doina Precup, Douglas L. Arnold, Tal Arbel:
IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI. IPMI 2015: 514-526 - [c107]Borja Balle, Prakash Panangaden, Doina Precup:
A Canonical Form for Weighted Automata and Applications to Approximate Minimization. LICS 2015: 701-712 - [c106]Gheorghe Comanici, Doina Precup, Prakash Panangaden:
Basis refinement strategies for linear value function approximation in MDPs. NIPS 2015: 2899-2907 - [c105]Philip Bachman, Doina Precup:
Data Generation as Sequential Decision Making. NIPS 2015: 3249-3257 - [c104]Pierre-Luc Bacon, Borja Balle, Doina Precup:
Learning and Planning with Timing Information in Markov Decision Processes. UAI 2015: 111-120 - [i13]Borja Balle, Prakash Panangaden, Doina Precup:
A Canonical Form for Weighted Automata and Applications to Approximate Minimization. CoRR abs/1501.06841 (2015) - [i12]Philip Bachman, Doina Precup:
Data Generation as Sequential Decision Making. CoRR abs/1506.03504 (2015) - [i11]Philip Bachman, David Krueger, Doina Precup:
Testing Visual Attention in Dynamic Environments. CoRR abs/1510.08949 (2015) - [i10]Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, Doina Precup:
Conditional Computation in Neural Networks for faster models. CoRR abs/1511.06297 (2015) - [i9]Lucas Lehnert, Doina Precup:
Policy Gradient Methods for Off-policy Control. CoRR abs/1512.04105 (2015) - 2014
- [j17]Sara M. McCarthy, Doina Precup:
Theoretical results on the effect of 'shortcut' actions in MDPs. Connect. Sci. 26(2): 179-193 (2014) - [j16]André da Motta Salles Barreto, Joelle Pineau, Doina Precup:
Policy Iteration Based on Stochastic Factorization. J. Artif. Intell. Res. 50: 763-803 (2014) - [c103]Negar Ghourchian, Doina Precup:
Analyzing User Trajectories from Mobile Device Data with Hierarchical Dirichlet Processes. Canadian AI 2014: 107-118 - [c102]Norm Ferns, Doina Precup, Sophia Knight:
Bisimulation for Markov Decision Processes through Families of Functional Expressions. Horizons of the Mind 2014: 319-342 - [c101]Nagesh K. Subbanna, Doina Precup, Tal Arbel:
Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI. CVPR 2014: 400-405 - [c100]Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel:
Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model. ECCV (1) 2014: 328-344 - [c99]Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel:
Multi-layer temporal graphical model for head pose estimation in real-world videos. ICIP 2014: 3392-3396 - [c98]Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt:
A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML 2014: 568-576 - [c97]Philip Bachman, Amir-massoud Farahmand, Doina Precup:
Sample-based approximate regularization. ICML 2014: 1926-1934 - [c96]Yuri Grinberg, Doina Precup, Michel Gendreau:
Optimizing Energy Production Using Policy Search and Predictive State Representations. NIPS 2014: 2969-2977 - [c95]Philip Bachman, Ouais Alsharif, Doina Precup:
Learning with Pseudo-Ensembles. NIPS 2014: 3365-3373 - [c94]Norman Ferns, Doina Precup:
Bisimulation Metrics are Optimal Value Functions. UAI 2014: 210-219 - [e1]Manuel Jorge Cardoso, Ivor J. A. Simpson, Tal Arbel, Doina Precup, Annemie Ribbens:
Bayesian and grAphical Models for Biomedical Imaging - First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. Lecture Notes in Computer Science 8677, Springer 2014, ISBN 978-3-319-12288-5 [contents] - [i8]Volodymyr Kuleshov, Doina Precup:
Algorithms for multi-armed bandit problems. CoRR abs/1402.6028 (2014) - [i7]Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh:
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions. CoRR abs/1407.0449 (2014) - [i6]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Practical Kernel-Based Reinforcement Learning. CoRR abs/1407.5358 (2014) - [i5]Philip Bachman, Ouais Alsharif, Doina Precup:
Learning with Pseudo-Ensembles. CoRR abs/1412.4864 (2014) - 2013
- [j15]Jordan Frank, Shie Mannor, Joelle Pineau, Doina Precup:
Time Series Analysis Using Geometric Template Matching. IEEE Trans. Pattern Anal. Mach. Intell. 35(3): 740-754 (2013) - [j14]Jordan Frank, Shie Mannor, Doina Precup:
Generating storylines from sensor data. Pervasive Mob. Comput. 9(6): 838-847 (2013) - [c93]Nastaran Jafarpour, Doina Precup, Masoumeh T. Izadi, David L. Buckeridge:
Using Hierarchical Mixture of Experts Model for Fusion of Outbreak Detection Methods. AMIA 2013 - [c92]Clement Gehring, Doina Precup:
Smart exploration in reinforcement learning using absolute temporal difference errors. AAMAS 2013: 1037-1044 - [c91]Arian Hosseinzadeh, Masoumeh T. Izadi, Aman Verma, Doina Precup, David L. Buckeridge:
Assessing the Predictability of Hospital Readmission Using Machine Learning. IAAI 2013: 1532-1538 - [c90]Yuri Grinberg, Doina Precup:
Average Reward Optimization Objective In Partially Observable Domains. ICML (1) 2013: 320-328 - [c89]Negar Ghourchian, Doina Precup:
Smart Classifier Selection for Activity Recognition on Wearable Devices. ICPRAM 2013: 581-585 - [c88]Nagesh K. Subbanna, Doina Precup, D. Louis Collins, Tal Arbel:
Hierarchical Probabilistic Gabor and MRF Segmentation of Brain Tumours in MRI Volumes. MICCAI (1) 2013: 751-758 - [c87]Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup:
Learning from Limited Demonstrations. NIPS 2013: 2859-2867 - [c86]Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup:
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces. NIPS 2013: 3030-3038 - [c85]Philip Bachman, Doina Precup:
Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization. ECML/PKDD (1) 2013: 241-256 - 2012
- [j13]Noa Agmon, Vikas Agrawal, David W. Aha, Yiannis Aloimonos, Donagh Buckley, Prashant Doshi, Christopher W. Geib, Floriana Grasso, Nancy L. Green, Benjamin Johnston, Burt Kaliski, Christopher Kiekintveld, Edith Law, Henry Lieberman, Ole J. Mengshoel, Ted Metzler, Joseph Modayil, Douglas W. Oard, Nilufer Onder, Barry O'Sullivan, Katerina Pastra, Doina Precup, Sowmya Ramachandran, Chris Reed, Sanem Sariel Talay, Ted Selker, Lokendra Shastri, Stephen F. Smith, Satinder Singh, Siddharth Srivastava, Gita Sukthankar, David C. Uthus, Mary-Anne Williams:
Reports of the AAAI 2011 Conference Workshops. AI Mag. 33(1): 57-70 (2012) - [j12]Philip A. Warrick, Emily F. Hamilton, Robert E. Kearney, Doina Precup:
A Machine Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery. AI Mag. 33(2): 79-90 (2012) - [j11]Susanne Still, Doina Precup:
An information-theoretic approach to curiosity-driven reinforcement learning. Theory Biosci. 131(3): 139-148 (2012) - [c84]Mahdi Milani Fard, Yuri Grinberg, Joelle Pineau, Doina Precup:
Compressed Least-Squares Regression on Sparse Spaces. AAAI 2012: 1054-1060 - [c83]Arian Hosseinzadeh, Masoumeh T. Izadi, Doina Precup, David L. Buckeridge:
Mining Administrative Data to Predict Falls in the Elderly Population. Canadian AI 2012: 305-311 - [c82]Meltem Demirkus, Doina Precup, James J. Clark, Tal Arbel:
Soft biometric trait classification from real-world face videos conditioned on head pose estimation. CVPR Workshops 2012: 130-137 - [c81]Doina Precup, Carlos A. Robles-Rubio, Karen A. Brown, Lara J. Kanbar, Jennifer Kaczmarek, Sanjay Chawla, Guilherme M. Sant'Anna, Robert E. Kearney:
Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability. EMBC 2012: 5630-5633 - [c80]Cosmin Paduraru, Doina Precup, Joelle Pineau, Gheorghe Comanici:
An Empirical Analysis of Off-policy Learning in Discrete MDPs. EWRL 2012: 89-102 - [c79]Doina Precup, Philip Bachman:
Improved Estimation in Time Varying Models. ICML 2012 - [c78]Amir Massoud Farahmand, Doina Precup:
Value Pursuit Iteration. NIPS 2012: 1349-1357 - [c77]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization. NIPS 2012: 1493-1501 - [c76]Gheorghe Comanici, Prakash Panangaden, Doina Precup:
On-the-Fly Algorithms for Bisimulation Metrics. QEST 2012: 94-103 - [c75]Yuri Grinberg, Doina Precup:
On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems. AISTATS 2012: 449-457 - [i4]Norman Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden:
Methods for computing state similarity in Markov Decision Processes. CoRR abs/1206.6836 (2012) - [i3]Norman Ferns, Prakash Panangaden, Doina Precup:
Metrics for Markov Decision Processes with Infinite State Spaces. CoRR abs/1207.1386 (2012) - [i2]Norman Ferns, Prakash Panangaden, Doina Precup:
Metrics for Finite Markov Decision Processes. CoRR abs/1207.4114 (2012) - [i1]Mahdi Milani Fard, Yuri Grinberg, Amir Massoud Farahmand, Joelle Pineau, Doina Precup:
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces. CoRR abs/1207.5554 (2012) - 2011
- [j10]Norm Ferns, Prakash Panangaden, Doina Precup:
Bisimulation Metrics for Continuous Markov Decision Processes. SIAM J. Comput. 40(6): 1662-1714 (2011) - [c74]Gheorghe Comanici, Doina Precup:
Basis Function Discovery Using Spectral Clustering and Bisimulation Metrics. AAAI 2011: 325-330 - [c73]Philip Bachman, Doina Precup:
Learning Compact Representations of Time-Varying Processes. AAAI 2011: 1748-1749 - [c72]Jordan Frank, Shie Mannor, Doina Precup:
Activity Recognition with Time-Delay Emobeddings. AAAI Spring Symposium: Computational Physiology 2011 - [c71]Gheorghe Comanici, Doina Precup:
Basis Function Discovery Using Spectral Clustering and Bisimulation Metrics. ALA 2011: 85-99 - [c70]Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, Doina Precup:
Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. AAMAS 2011: 761-768 - [c69]Gheorghe Comanici, Doina Precup:
Basis function discovery using spectral clustering and bisimulation metrics. AAMAS 2011: 1079-1080 - [c68]Pablo Samuel Castro, Doina Precup:
Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics. EWRL 2011: 140-152 - [c67]Cosmin Paduraru, Doina Precup, Joelle Pineau:
A Framework for Computing Bounds for the Return of a Policy. EWRL 2011: 201-212 - [c66]Nagesh K. Subbanna, Simon J. Francis, Doina Precup, D. Louis Collins, Douglas L. Arnold, Tal Arbel:
Adapted MRF Segmentation of Multiple Sclerosis Lesions Using Local Contextual Information. MIUA 2011: 351-356 - [c65]André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Reinforcement Learning using Kernel-Based Stochastic Factorization. NIPS 2011: 720-728 - [c64]Jordan Frank, Shie Mannor, Doina Precup:
Activity Recognition with Mobile Phones. ECML/PKDD (3) 2011: 630-633 - [c63]Monica Dinculescu, Christopher Hundt, Prakash Panangaden, Joelle Pineau, Doina Precup:
The Duality of State and Observation in Probabilistic Transition Systems. TbiLLC 2011: 206-230 - 2010
- [j9]Philip A. Warrick, Emily F. Hamilton, Doina Precup, Robert E. Kearney:
Classification of Normal and Hypoxic Fetuses From Systems Modeling of Intrapartum Cardiotocography. IEEE Trans. Biomed. Eng. 57(4): 771-779 (2010) - [c62]Pablo Samuel Castro, Doina Precup:
Using Bisimulation for Policy Transfer in MDPs. AAAI 2010: 1065-1070 - [c61]Jordan Frank, Shie Mannor, Doina Precup:
Activity and Gait Recognition with Time-Delay Embeddings. AAAI 2010: 1581-1586 - [c60]Gheorghe Comanici, Doina Precup:
Optimal policy switching algorithms for reinforcement learning. AAMAS 2010: 709-714 - [c59]Pablo Samuel Castro, Doina Precup:
Using bisimulation for policy transfer in MDPs. AAMAS 2010: 1399-1400 - [c58]Robert West, Doina Precup, Joelle Pineau:
Automatically suggesting topics for augmenting text documents. CIKM 2010: 929-938 - [c57]Prakash Panangaden, Caitlin Phillips, Doina Precup, Mehrnoosh Sadrzadeh:
An Algebraic Approach to Dynamic Epistemic Logic. Description Logics 2010 - [c56]Jordan Frank, Shie Mannor, Doina Precup:
A novel similarity measure for time series data with applications to gait and activity recognition. UbiComp (Adjunct Papers) 2010: 407-408 - [c55]Philip A. Warrick, Emily F. Hamilton, Robert E. Kearney, Doina Precup:
A Machine Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery. IAAI 2010: 1865-1870 - [c54]Monica Dinculescu, Doina Precup:
Approximate Predictive Representations of Partially Observable Systems. ICML 2010: 895-902 - [c53]Pablo Samuel Castro, Doina Precup:
Smarter Sampling in Model-Based Bayesian Reinforcement Learning. ECML/PKDD (1) 2010: 200-214 - [c52]Fabian Kaelin, Doina Precup:
A Study of Approximate Inference in Probabilistic Relational Models. ACML 2010: 315-330
2000 – 2009
- 2009
- [j8]Philip A. Warrick, Emily F. Hamilton, Doina Precup, Robert E. Kearney:
Identification of the Dynamic Relationship Between Intrapartum Uterine Pressure and Fetal Heart Rate for Normal and Hypoxic Fetuses. IEEE Trans. Biomed. Eng. 56(6): 1587-1597 (2009) - [c51]Robert West, Doina Precup, Joelle Pineau:
Completing wikipedia's hyperlink structure through dimensionality reduction. CIKM 2009: 1097-1106 - [c50]Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora:
Fast gradient-descent methods for temporal-difference learning with linear function approximation. ICML 2009: 993-1000 - [c49]Robert West, Joelle Pineau, Doina Precup:
Wikispeedia: An Online Game for Inferring Semantic Distances between Concepts. IJCAI 2009: 1598-1603 - [c48]Pablo Samuel Castro, Prakash Panangaden, Doina Precup:
Equivalence Relations in Fully and Partially Observable Markov Decision Processes. IJCAI 2009: 1653-1658 - [c47]Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton:
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. NIPS 2009: 1204-1212 - [c46]Sami Zhioua, Doina Precup, François Laviolette, Josée Desharnais:
Learning the Difference between Partially Observable Dynamical Systems. ECML/PKDD (2) 2009: 664-677 - 2008
- [j7]Rupert Brooks, Tal Arbel, Doina Precup:
Anytime similarity measures for faster alignment. Comput. Vis. Image Underst. 110(3): 378-389 (2008) - [c45]Masoumeh T. Izadi, Doina Precup:
Point-Based Planning for Predictive State Representations. Canadian AI 2008: 126-137 - [c44]Jordan Frank, Shie Mannor, Doina Precup:
Reinforcement learning in the presence of rare events. ICML 2008: 336-343 - [c43]Jonathan Taylor, Doina Precup, Prakash Panangaden:
Bounding Performance Loss in Approximate MDP Homomorphisms. NIPS 2008: 1649-1656 - 2007
- [j6]Robin Jaulmes, Joelle Pineau, Doina Precup:
Apprentissage actif dans les processus décisionnels de Markov partiellement observables L'algorithme MEDUSA. Rev. d'Intelligence Artif. 21(1): 9-34 (2007) - [c42]Christopher Hundt, Prakash Panangaden, Joelle Pineau, Doina Precup:
Representing Systems with Hidden State. AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development 2007: 17-23 - [c41]Robin Jaulmes, Joelle Pineau, Doina Precup:
A formal framework for robot learning and control under model uncertainty. ICRA 2007: 2104-2110 - [c40]Marc G. Bellemare, Doina Precup:
Context-Driven Predictions. IJCAI 2007: 250-255 - [c39]Rupert Brooks, Tal Arbel, Doina Precup:
Fast Image Alignment Using Anytime Algorithms. IJCAI 2007: 2078-2083 - [c38]Pablo Samuel Castro, Doina Precup:
Using Linear Programming for Bayesian Exploration in Markov Decision Processes. IJCAI 2007: 2437-2442 - 2006
- [c37]Christopher Hundt, Prakash Panangaden, Joelle Pineau, Doina Precup:
Representing Systems with Hidden State. AAAI 2006: 368-374 - [c36]Masoumeh T. Izadi, Doina Precup, Danielle Azar:
Belief Selection in Point-Based Planning Algorithms for POMDPs. Canadian AI 2006: 383-394 - [c35]Ricard Gavaldà, Philipp W. Keller, Joelle Pineau, Doina Precup:
PAC-Learning of Markov Models with Hidden State. ECML 2006: 150-161 - [c34]Philip A. Warrick, Robert E. Kearney, Doina Precup, Emily F. Hamilton:
Linear models of intrapartum uterine pressure-fetal heart rate interaction for the normal and hypoxic fetus. EMBC 2006: 6434-6437 - [c33]Philipp W. Keller, Shie Mannor, Doina Precup:
Automatic basis function construction for approximate dynamic programming and reinforcement learning. ICML 2006: 449-456 - [c32]Beibei Zou, Xuesong Ma, Bettina Kemme, Glen Newton, Doina Precup:
Data Mining Using Relational Database Management Systems. PAKDD 2006: 657-667 - [c31]Norm Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden:
Methods for Computing State Similarity in Markov Decision Processes. UAI 2006 - 2005
- [j5]Ion Muslea, Virginia Dignum, Daniel D. Corkill, Catholijn M. Jonker, Frank Dignum, Silvia Coradeschi, Alessandro Saffiotti, Dan Fu, Jeff Orkin, William Cheetham, Kai Goebel, Piero P. Bonissone, Leen-Kiat Soh, Randolph M. Jones, Robert E. Wray III, Matthias Scheutz, Daniela Pucci de Farias, Shie Mannor, Georgios Theocharous, Doina Precup, Bamshad Mobasher, Sarabjot S. Anand, Bettina Berendt, Andreas Hotho, Hans W. Guesgen, Michael T. Rosenstein, Mohammad Ghavamzadeh:
The Workshop Program at the Nineteenth National Conference on Artificial Intelligence. AI Mag. 26(1): 103-108 (2005) - [c30]Masoumeh T. Izadi, Doina Precup:
Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes. ECML 2005: 593-600 - [c29]Robin Jaulmes, Joelle Pineau, Doina Precup:
Active Learning in Partially Observable Markov Decision Processes. ECML 2005: 601-608 - [c28]Masoumeh T. Izadi, Doina Precup:
Model minimization by linear PSR. IJCAI 2005: 1749-1750 - [c27]Masoumeh T. Izadi, Ajit V. Rajwade, Doina Precup:
Using core beliefs for point-based value iteration. IJCAI 2005: 1751-1753 - [c26]Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder Singh:
Off-policy Learning with Options and Recognizers. NIPS 2005: 1097-1104 - [c25]Alexandre Bouchard-Côté, Norm Ferns, Prakash Panangaden, Doina Precup:
An approximation algorithm for labelled Markov processes: towards realistic approximation. QEST 2005: 54-62 - [c24]Norm Ferns, Prakash Panangaden, Doina Precup:
Metrics for Markov Decision Processes with Infinite State Spaces. UAI 2005: 201-208 - 2004
- [j4]Doina Precup, Paul E. Utgoff:
Classification Using Phi-Machines and Constructive Function Approximation. Mach. Learn. 55(1): 31-52 (2004) - [j3]Philipp W. Keller, Felix-Olivier Duguay, Doina Precup:
Redagent: winner of TAC SCM 2003. SIGecom Exch. 4(3): 1-8 (2004) - [c23]Norm Ferns, Prakash Panangaden, Doina Precup:
Metrics for Finite Markov Decision Processes. AAAI 2004: 950-951 - [c22]Philipp W. Keller, Felix-Olivier Duguay, Doina Precup:
RedAgent-2003: An Autonomous Market-Based Supply-Chain Management Agent. AAMAS 2004: 1182-1189 - [c21]Bohdana Ratitch, Doina Precup:
Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning. ECML 2004: 347-358 - [c20]Norm Ferns, Prakash Panangaden, Doina Precup:
Metrics for Finite Markov Decision Processes. UAI 2004: 162-169 - 2003
- [c19]Bohdana Ratitch, Doina Precup:
Using MDP Characteristics to Guide Exploration in Reinforcement Learning. ECML 2003: 313-324 - [c18]François Rivest, Doina Precup:
Combining TD-learning with Cascade-correlation Networks. ICML 2003: 632-639 - [c17]Masoumeh T. Izadi, Doina Precup:
A Planning Algorithm for Predictive State Representations. IJCAI 2003: 1520-1521 - 2002
- [j2]Ioan Alfred Letia, Doina Precup:
Developing Collaborative Golog Agents by Reinforcement Learning. Int. J. Artif. Intell. Tools 11(2): 233-246 (2002) - [c16]Bohdana Ratitch, Doina Precup:
Characterizing Markov Decision Processes. ECML 2002: 391-404 - [c15]Danielle Azar, Doina Precup, Salah Bouktif, Balázs Kégl, Houari A. Sahraoui:
Combining and Adapting Software Quality Predictive Models by Genetic Algorithms. ASE 2002: 285-288 - [c14]Theodore J. Perkins, Doina Precup:
A Convergent Form of Approximate Policy Iteration. NIPS 2002: 1595-1602 - [c13]Martin Stolle, Doina Precup:
Learning Options in Reinforcement Learning. SARA 2002: 212-223 - 2001
- [c12]Doina Precup, Richard S. Sutton, Sanjoy Dasgupta:
Off-Policy Temporal Difference Learning with Function Approximation. ICML 2001: 417-424 - [c11]Ioan Alfred Letia, Doina Precup:
Developing Collaborative Golog Agents by Reinforcement Learning. ICTAI 2001: 195-202 - 2000
- [c10]Catherine C. McGeoch, Peter Sanders, Rudolf Fleischer, Paul R. Cohen, Doina Precup:
Using Finite Experiments to Study Asymptotic Performance. Experimental Algorithmics 2000: 93-126 - [c9]Doina Precup, Richard S. Sutton, Satinder Singh:
Eligibility Traces for Off-Policy Policy Evaluation. ICML 2000: 759-766
1990 – 1999
- 1999
- [j1]Richard S. Sutton, Doina Precup, Satinder Singh:
Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artif. Intell. 112(1-2): 181-211 (1999) - 1998
- [c8]Doina Precup, Richard S. Sutton, Satinder Singh:
Theoretical Results on Reinforcement Learning with Temporally Abstract Options. ECML 1998: 382-393 - [c7]Doina Precup, Paul E. Utgoff:
Classification Using Phi-Machines and Constructive Function Approximation. ICML 1998: 439-444 - [c6]Richard S. Sutton, Doina Precup, Satinder Singh:
Intra-Option Learning about Temporally Abstract Actions. ICML 1998: 556-564 - [c5]Richard S. Sutton, Satinder Singh, Doina Precup, Balaraman Ravindran:
Improved Switching among Temporally Abstract Actions. NIPS 1998: 1066-1072 - 1997
- [c4]Doina Precup, Richard S. Sutton:
Exponentiated Gradient Methods for Reinforcement Learning. ICML 1997: 272-277 - [c3]Catherine C. McGeoch, Doina Precup, Paul R. Cohen:
How to Find Big-Oh in Your Data Set (and How Not to). IDA 1997: 41-52 - [c2]J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Doina Precup, Darko Stefanovic, Carla E. Brodley, David Scheeff:
Learning to Schedule Straight-Line Code. NIPS 1997: 929-935 - [c1]Doina Precup, Richard S. Sutton:
Multi-time Models for Temporally Abstract Planning. NIPS 1997: 1050-1056
Coauthor Index
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