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2020 – today
- 2022
[c22]Kai Wang, Lily Xu, Andrew Perrault, Michael K. Reiter, Milind Tambe:
Coordinating Followers to Reach Better Equilibria: End-to-End Gradient Descent for Stackelberg Games. AAAI 2022: 5219-5227- [c21]Ju-Seung Byun, Andrew Perrault:
Training Transition Policies via Distribution Matching for Complex Tasks. ICLR 2022 - [i15]Sanket Shah, Bryan Wilder, Andrew Perrault, Milind Tambe:
Learning (Local) Surrogate Loss Functions for Predict-Then-Optimize Problems. CoRR abs/2203.16067 (2022) - [i14]Ju-Seung Byun, Andrew Perrault:
Normality-Guided Distributional Reinforcement Learning for Continuous Control. CoRR abs/2208.13125 (2022) - 2021
- [c20]Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe:
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security. AAAI 2021: 14974-14982 - [c19]Jackson A. Killian, Andrew Perrault, Milind Tambe:
Beyond "To Act or Not to Act": Fast Lagrangian Approaches to General Multi-Action Restless Bandits. AAMAS 2021: 710-718 - [c18]Aditya Mate, Andrew Perrault, Milind Tambe:
Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits. AAMAS 2021: 880-888 - [c17]Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe:
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning. NeurIPS 2021: 8795-8806 - [c16]Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe:
Robust reinforcement learning under minimax regret for green security. UAI 2021: 257-267 - [i13]Kai Wang, Lily Xu, Andrew Perrault, Michael K. Reiter, Milind Tambe:
Coordinating Followers to Reach Better Equilibria: End-to-End Gradient Descent for Stackelberg Games. CoRR abs/2106.03278 (2021) - [i12]Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe:
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning. CoRR abs/2106.03279 (2021) - [i11]Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe:
Robust Reinforcement Learning Under Minimax Regret for Green Security. CoRR abs/2106.08413 (2021) - [i10]Ju-Seung Byun, Andrew Perrault:
Training Transition Policies via Distribution Matching for Complex Tasks. CoRR abs/2110.04357 (2021) - 2020
- [j1]Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe:
Artificial Intelligence for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline. AI Mag. 41(4): 3-16 (2020) - [c15]Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe:
End-to-End Game-Focused Learning of Adversary Behavior in Security Games. AAAI 2020: 1378-1386 - [c14]Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe:
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning. AAAI 2020: 2226-2235 - [c13]Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Alpan Raval, Milind Tambe:
Who and When to Screen: Multi-Round Active Screening for Network Recurrent Infectious Diseases Under Uncertainty. AAMAS 2020: 992-1000 - [c12]Kai Wang, Andrew Perrault, Aditya Mate, Milind Tambe:
Scalable Game-Focused Learning of Adversary Models: Data-to-Decisions in Network Security Games. AAMAS 2020: 1449-1457 - [c11]Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe:
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems. NeurIPS 2020 - [c10]Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe:
Collapsing Bandits and Their Application to Public Health Intervention. NeurIPS 2020 - [c9]Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel J. Kochenderfer, Milind Tambe, Yevgeniy Vorobeychik:
Robust Spatial-Temporal Incident Prediction. UAI 2020: 360-369 - [i9]Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe:
AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline. CoRR abs/2001.00088 (2020) - [i8]Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe:
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems. CoRR abs/2006.10815 (2020) - [i7]Lily Xu, Andrew Perrault, Andrew J. Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Milind Tambe:
Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers. CoRR abs/2006.12411 (2020) - [i6]Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe:
Collapsing Bandits and Their Application to Public Health Interventions. CoRR abs/2007.04432 (2020) - [i5]Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe:
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security. CoRR abs/2009.06560 (2020)
2010 – 2019
- 2019
- [c8]Andrew Perrault, Craig Boutilier:
Experiential Preference Elicitation for Autonomous Heating and Cooling Systems. AAMAS 2019: 431-439 - [i4]Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe:
Decision-Focused Learning of Adversary Behavior in Security Games. CoRR abs/1903.00958 (2019) - [i3]Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Milind Tambe:
Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty. CoRR abs/1903.06113 (2019) - [i2]Sanket Shah
, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe:
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning. CoRR abs/1911.08799 (2019) - 2018
- [b1]Andrew Perrault:
Developing and Coordinating Autonomous Agents for Efficient Electricity Markets. University of Toronto, Canada, 2018 - 2017
- [c7]Andrew Perrault, Craig Boutilier:
Multiple-Profile Prediction-of-Use Games. AAMAS Workshops (Selected Papers) 2017: 275-295 - [c6]Andrew Perrault, Craig Boutilier:
Multiple-Profile Prediction-of-Use Games. AAMAS 2017: 1688-1690 - [c5]Andrew Perrault, Craig Boutilier:
Multiple-Profile Prediction-of-Use Games. IJCAI 2017: 366-373 - 2016
- [c4]Andrew Perrault, Joanna Drummond, Fahiem Bacchus:
Strategy-Proofness in the Stable Matching Problem with Couples. AAMAS 2016: 132-140 - 2015
- [c3]Joanna Drummond, Andrew Perrault, Fahiem Bacchus:
SAT Is an Effective and Complete Method for Solving Stable Matching Problems with Couples. IJCAI 2015: 518-525 - [c2]Andrew Perrault, Craig Boutilier:
Approximately Stable Pricing for Coordinated Purchasing of Electricity. IJCAI 2015: 2624-2631 - [i1]Andrew Perrault, Joanna Drummond, Fahiem Bacchus:
Exploring Strategy-Proofness, Uniqueness, and Pareto Optimality for the Stable Matching Problem with Couples. CoRR abs/1505.03463 (2015) - 2014
- [c1]Andrew Perrault, Craig Boutilier:
Efficient coordinated power distribution on private infrastructure. AAMAS 2014: 805-812
Coauthor Index
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