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- affiliation: McGill University, Montreal, Canada
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2020 – today
- 2023
[j38]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)- [j37]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) - [c219]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 - [c218]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 - [c217]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 - [c216]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 - [i156]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) - [i155]Safa Alver, Doina Precup:
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning. CoRR abs/2301.10119 (2023) - [i154]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) - [i153]Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, Ankit Anand:
Accelerating exploration and representation learning with offline pre-training. CoRR abs/2304.00046 (2023) - [i152]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) - [i151]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) - [i150]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) - [i149]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) - [i148]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) - [i147]Veronica Chelu, Tom Zahavy, Arthur Guez, Doina Precup, Sebastian Flennerhag:
Optimism and Adaptivity in Policy Optimization. CoRR abs/2306.10587 (2023) - [i146]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) - [i145]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) - [i144]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) - [i143]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) - 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]