default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDoina Precup
Person information
- affiliation: McGill University, Montreal, Canada
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j40]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)- [j39]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) - [c237]Jonathan Lebensold, Doina Precup, Borja Balle:
On the Privacy of Selection Mechanisms with Gaussian Noise. AISTATS 2024: 1495-1503 - [c236]Gandharv Patil, Aditya Mahajan, Doina Precup:
On learning history-based policies for controlling Markov decision processes. AISTATS 2024: 3511-3519 - [c235]Jonathan Colaço Carr, Prakash Panangaden, Doina Precup:
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies. AISTATS 2024: 3916-3924 - [c234]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 - [c233]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 - [c232]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 - [c231]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 - [c230]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 - [c229]David Venuto, Mohammad Sami Nur Islam, Martin Klissarov, Doina Precup, Sherry Yang, Ankit Anand:
Code as Reward: Empowering Reinforcement Learning with VLMs. ICML 2024 - [i179]Chenqing Hua, Connor W. Coley, Guy Wolf, Doina Precup, Shuangjia Zheng:
Effective Protein-Protein Interaction Exploration with PPIretrieval. CoRR abs/2402.03675 (2024) - [i178]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) - [i177]Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio:
QGFN: Controllable Greediness with Action Values. CoRR abs/2402.05234 (2024) - [i176]Jonathan Lebensold, Doina Precup, Borja Balle:
On the Privacy of Selection Mechanisms with Gaussian Noise. CoRR abs/2402.06137 (2024) - [i175]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) - [i174]Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio:
Discrete Probabilistic Inference as Control in Multi-path Environments. CoRR abs/2402.10309 (2024) - [i173]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) - [i172]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) - [i171]Arushi Jain, Josiah P. Hanna, Doina Precup:
Adaptive Exploration for Data-Efficient General Value Function Evaluations. CoRR abs/2405.07838 (2024) - [i170]Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup:
Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents. CoRR abs/2405.16899 (2024) - [i169]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) - [i168]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) - [i167]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) - [i166]Veronica Chelu, Doina Precup:
Functional Acceleration for Policy Mirror Descent. CoRR abs/2407.16602 (2024) - [i165]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) - 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) - [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
- [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]