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Andrew Perrault
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2020 – today
- 2024
- [c30]Sanket Shah, Bryan Wilder, Andrew Perrault, Milind Tambe:
Leaving the Nest: Going beyond Local Loss Functions for Predict-Then-Optimize. AAAI 2024: 14902-14909 - [c29]Adam Zychowski, Andrew Perrault, Jacek Mandziuk:
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret. AAAI 2024: 21869-21877 - [c28]Yi Mao, Andrew Perrault:
Time-Constrained Restless Multi-Armed Bandits with Applications to City Service Scheduling. AAMAS 2024: 2375-2377 - [c27]Ju-Seung Byun, Jiyun Chun, Jihyung Kil, Andrew Perrault:
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback. EMNLP 2024: 4410-4430 - [i23]Xi Chen, Zhihui Zhu, Andrew Perrault:
The Distributional Reward Critic Architecture for Perturbed-Reward Reinforcement Learning. CoRR abs/2401.05710 (2024) - [i22]Jingyi Chen, Ju-Seung Byun, Micha Elsner, Andrew Perrault:
Reinforcement Learning for Fine-tuning Text-to-speech Diffusion Models. CoRR abs/2405.14632 (2024) - [i21]Ju-Seung Byun, Andrew Perrault:
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales. CoRR abs/2405.17618 (2024) - [i20]Ju-Seung Byun, Jiyun Chun, Jihyung Kil, Andrew Perrault:
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback. CoRR abs/2407.00087 (2024) - [i19]Anurag Kumar, Andrew Perrault, Donald S. Williamson:
Using RLHF to align speech enhancement approaches to mean-opinion quality scores. CoRR abs/2410.13182 (2024) - 2023
- [c26]Andrew Perrault:
Monitoring and Intervening on Large Populations of Weakly Coupled Processes with Social Impact Applications. AAAI 2023: 15450 - [c25]Xueqiao Peng, Jiaqi Xu, Xi Chen, Dinh Song An Nguyen, Andrew Perrault:
Using Reinforcement Learning for Multi-Objective Cluster-Level Optimization of Non-Pharmaceutical Interventions for Infectious Disease. ML4H@NeurIPS 2023: 445-460 - [c24]Pulkit Arya, Madeleine Bloomquist, Subhankar Chakraborty, Andrew Perrault, William Schuler, Eric Fosler-Lussier, Michael White:
Bootstrapping a Conversational Guide for Colonoscopy Prep. SIGDIAL 2023: 413-420 - [i18]Sanket Shah, Andrew Perrault, Bryan Wilder, Milind Tambe:
Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize. CoRR abs/2305.16830 (2023) - [i17]Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Y. Berger-Wolf, Tanya Birch
, Elizabeth Bondi-Kelly, Justin Brashares, Melissa S. Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe:
Reflections from the Workshop on AI-Assisted Decision Making for Conservation. CoRR abs/2307.08774 (2023) - [i16]Adam Zychowski, Andrew Perrault, Jacek Mandziuk:
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret. CoRR abs/2312.09078 (2023) - 2022
- [c23]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 - [c22]Ju-Seung Byun, Andrew Perrault:
Training Transition Policies via Distribution Matching for Complex Tasks. ICLR 2022 - [c21]Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe:
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses. NeurIPS 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
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
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
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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last updated on 2024-12-01 00:18 CET by the dblp team
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