Brunskill, Emma

86 publications

AAAI 2025 Cost-Aware Near-Optimal Policy Learning Joy He-Yueya, Jonathan Lee, Matthew Jörke, Emma Brunskill

ICLR 2024 Adaptive Instrument Design for Indirect Experiments Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill

CHIL 2024 Adaptive Interventions with User-Defined Goals for Health Behavior Change Aishwarya Mandyam, Matthew Jörke, William Denton, Barbara E. Engelhardt, Emma Brunskill

TMLR 2024 Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

NeurIPSW 2024 Information Directed Tree Search: Reasoning and Planning with Language Agents Yash Chandak, HyunJi Nam, Allen Nie, Jonathan Lee, Emma Brunskill

NeurIPS 2024 OPERA: Automatic Offline Policy Evaluation with Re-Weighted Aggregates of Multiple Estimators Allen Nie, Yash Chandak, Christina J. Yuan, Anirudhan Badrinath, Yannis Flet-Berliac, Emma Brunskill

MLJ 2024 Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task Sherry Ruan, Allen Nie, William Steenbergen, Jiayu He, J. Q. Zhang, Meng Guo, Yao Liu, Kyle Dang Nguyen, Catherine Y. Wang, Rui Ying, James A. Landay, Emma Brunskill

NeurIPS 2023 Experiment Planning with Function Approximation Aldo Pacchiano, Jonathan Lee, Emma Brunskill

ICMLW 2023 In-Context Decision-Making from Supervised Pretraining Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

AAAI 2023 Model-Based Offline Reinforcement Learning with Local Misspecification Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill

NeurIPS 2023 Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill

NeurIPS 2023 Supervised Pretraining Can Learn In-Context Reinforcement Learning Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

NeurIPS 2023 Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill

NeurIPSW 2023 Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets Anirudhan Badrinath, Allen Nie, Yannis Flet-Berliac, Emma Brunskill

AAAI 2022 Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill

NeurIPS 2022 Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data Allen Nie, Yannis Flet-Berliac, Deon Jordan, William Steenbergen, Emma Brunskill

NeurIPS 2022 Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits Tong Mu, Yash Chandak, Tatsunori B Hashimoto, Emma Brunskill

NeurIPS 2022 Giving Feedback on Interactive Student Programs with Meta-Exploration Evan Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn

ICMLW 2022 Giving Feedback on Interactive Student Programs with Meta-Exploration Evan Zheran Liu, Moritz Pascal Stephan, Allen Nie, Christopher J Piech, Emma Brunskill, Chelsea Finn

CHIL 2022 Identification of Subgroups with Similar Benefits in Off-Policy Policy Evaluation Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill

NeurIPS 2022 Off-Policy Evaluation for Action-Dependent Non-Stationary Environments Yash Chandak, Shiv Shankar, Nathaniel Bastian, Bruno da Silva, Emma Brunskill, Philip S. Thomas

UAI 2022 Offline Policy Optimization with Eligible Actions Yao Liu, Yannis Flet-Berliac, Emma Brunskill

NeurIPS 2022 Oracle Inequalities for Model Selection in Offline Reinforcement Learning Jonathan N Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill

AISTATS 2021 Online Model Selection for Reinforcement Learning with Function Approximation Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill

NeurIPS 2021 Design of Experiments for Stochastic Contextual Linear Bandits Andrea Zanette, Kefan Dong, Jonathan N Lee, Emma Brunskill

NeurIPS 2021 Play to Grade: Testing Coding Games as Classifying Markov Decision Process Allen Nie, Emma Brunskill, Chris Piech

MLHC 2021 Power Constrained Bandits Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan Murphy, Finale Doshi-Velez

NeurIPS 2021 Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning Andrea Zanette, Martin J Wainwright, Emma Brunskill

NeurIPS 2021 Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes HyunJi Alex Nam, Scott Fleming, Emma Brunskill

NeurIPS 2021 Universal Off-Policy Evaluation Yash Chandak, Scott Niekum, Bruno da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas

AAAI 2020 Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill

AISTATS 2020 Frequentist Regret Bounds for Randomized Least-Squares Value Iteration Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric

ICML 2020 Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, Finale Doshi-Velez

ICML 2020 Learning near Optimal Policies with Low Inherent Bellman Error Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill

NeurIPS 2020 Off-Policy Policy Evaluation for Sequential Decisions Under Unobserved Confounding Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

NeurIPS 2020 Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration Andrea Zanette, Alessandro Lazaric, Mykel J Kochenderfer, Emma Brunskill

NeurIPS 2020 Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

AISTATS 2020 Sublinear Optimal Policy Value Estimation in Contextual Bandits Weihao Kong, Emma Brunskill, Gregory Valiant

JMLR 2020 Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau

ICML 2020 Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling Yao Liu, Pierre-Luc Bacon, Emma Brunskill

NeurIPS 2019 Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model Andrea Zanette, Mykel J Kochenderfer, Emma Brunskill

ICML 2019 Combining Parametric and Nonparametric Models for Off-Policy Evaluation Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez

UAI 2019 Fake It till You Make It: Learning-Compatible Performance Support Jonathan Bragg, Emma Brunskill

ICLR 2019 Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure Karan Goel, Emma Brunskill

NeurIPS 2019 Limiting Extrapolation in Linear Approximate Value Iteration Andrea Zanette, Alessandro Lazaric, Mykel J Kochenderfer, Emma Brunskill

ICMLW 2019 Off-Policy Policy Gradient with State Distribution Correction Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

UAI 2019 Off-Policy Policy Gradient with Stationary Distribution Correction Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

NeurIPS 2019 Offline Contextual Bandits with High Probability Fairness Guarantees Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas

ICML 2019 Policy Certificates: Towards Accountable Reinforcement Learning Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill

ICML 2019 Separating Value Functions Across Time-Scales Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill, Joelle Pineau, Yann Ollivier

ICML 2019 Tighter Problem-Dependent Regret Bounds in Reinforcement Learning Without Domain Knowledge Using Value Function Bounds Andrea Zanette, Emma Brunskill

ICML 2018 Decoupling Gradient-like Learning Rules from Representations Philip Thomas, Christoph Dann, Emma Brunskill

IJCAI 2018 Importance Sampling for Fair Policy Selection Shayan Doroudi, Philip S. Thomas, Emma Brunskill

ICML 2018 Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs Andrea Zanette, Emma Brunskill

NeurIPS 2018 Representation Balancing MDPs for Off-Policy Policy Evaluation Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A Faisal, Finale Doshi-Velez, Emma Brunskill

UAI 2017 Importance Sampling for Fair Policy Selection Shayan Doroudi, Philip S. Thomas, Emma Brunskill

AAAI 2017 Importance Sampling with Unequal Support Philip S. Thomas, Emma Brunskill

AAAI 2017 Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, Emma Brunskill

NeurIPS 2017 Regret Minimization in MDPs with Options Without Prior Knowledge Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill

IJCAI 2017 Sample Efficient Policy Search for Optimal Stopping Domains Karan Goel, Christoph Dann, Emma Brunskill

AISTATS 2017 Trading Off Rewards and Errors in Multi-Armed Bandits Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu

NeurIPS 2017 Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann, Tor Lattimore, Emma Brunskill

NeurIPS 2017 Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation Zhaohan Guo, Philip S. Thomas, Emma Brunskill

AAAI 2017 Where to Add Actions in Human-in-the-Loop Reinforcement Learning Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic

AISTATS 2016 A PAC RL Algorithm for Episodic POMDPs Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill

ICML 2016 Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning Philip Thomas, Emma Brunskill

IJCAI 2016 Efficient Bayesian Clustering for Reinforcement Learning Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic

ICML 2016 Energetic Natural Gradient Descent Philip Thomas, Bruno Castro Silva, Christoph Dann, Emma Brunskill

IJCAI 2016 Latent Contextual Bandits and Their Application to Personalized Recommendations for New Users Li Zhou, Emma Brunskill

AAAI 2016 Offline Evaluation of Online Reinforcement Learning Algorithms Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic

IJCAI 2016 Questimator: Generating Knowledge Assessments for Arbitrary Topics Qi Guo, Chinmay Kulkarni, Aniket Kittur, Jeffrey P. Bigham, Emma Brunskill

AAAI 2015 Concurrent PAC RL Zhaohan Guo, Emma Brunskill

NeurIPS 2015 Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning Christoph Dann, Emma Brunskill

AAAI 2015 The Queue Method: Handling Delay, Heuristics, Prior Data, and Evaluation in Bandits Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic

ICML 2014 Online Stochastic Optimization Under Correlated Bandit Feedback Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

ICML 2014 PAC-Inspired Option Discovery in Lifelong Reinforcement Learning Emma Brunskill, Lihong Li

ECML-PKDD 2013 Regret Bounds for Reinforcement Learning with Policy Advice Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

UAI 2013 Sample Complexity of Multi-Task Reinforcement Learning Emma Brunskill, Lihong Li

NeurIPS 2013 Sequential Transfer in Multi-Armed Bandit with Finite Set of Models Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill

UAI 2012 Incentive Decision Processes Sashank Jakkam Reddi, Emma Brunskill

JAIR 2011 Efficient Planning Under Uncertainty with Macro-Actions Ruijie He, Emma Brunskill, Nicholas Roy

AAAI 2010 PUMA: Planning Under Uncertainty with Macro-Actions Ruijie He, Emma Brunskill, Nicholas Roy

UAI 2010 RAPID: A Reachable Anytime Planner for Imprecisely-Sensed Domains Emma Brunskill, Stuart Russell

JMLR 2009 Provably Efficient Learning with Typed Parametric Models Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy

UAI 2008 CORL: A Continuous-State Offset-Dynamics Reinforcement Learner Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy

AAAI 2007 Continuous State POMDPs for Object Manipulation Tasks Emma Brunskill