Emma Brunskill

ORCID

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 167

Top Venues

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Amelia Leon, Allen Nie, Yash Chandak, Emma Brunskill
Estimating the Causal Treatment Effect of Unproductive Persistence. LAK (2024)
Allen Nie, Yash Chandak, Miroslav Suzara, Ali Malik, Juliette Woodrow, Matt Peng, Mehran Sahami, Emma Brunskill, Chris Piech
The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances. CoRR (2024)
Hyunji Alex Nam, Yash Chandak, Emma Brunskill
Short-Long Policy Evaluation with Novel Actions. CoRR (2024)
Emma Brunskill, Kole A. Norberg, Stephen Fancsali, Steven Ritter
Examining the Use of an AI-Powered Teacher Orchestration Tool at Scale. L@S (2024)
Matthew Jörke, Shardul Sapkota, Lyndsea Warkenthien, Niklas Vainio, Paul Schmiedmayer, Emma Brunskill, James A. Landay
Supporting Physical Activity Behavior Change with LLM-Based Conversational Agents. CoRR (2024)
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill
Adaptive Instrument Design for Indirect Experiments. ICLR (2024)
Evan Zheran Liu, David Yuan, Ahmed Ahmed, Elyse Cornwall, Juliette Woodrow, Kaylee Burns, Allen Nie, Emma Brunskill, Chris Piech, Chelsea Finn
A Fast and Accurate Machine Learning Autograder for the Breakout Assignment. SIGCSE (1) (2024)
Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. 2024 (2024)
Joy He-Yueya, Wanjing Anya Ma, Kanishk Gandhi, Benjamin W. Domingue, Emma Brunskill, Noah D. Goodman
Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models. CoRR (2024)
Ryan Louie, Ananjan Nandi, William Fang, Cheng Chang, Emma Brunskill, Diyi Yang
Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles. CoRR (2024)
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
Reinforcement learning tutor better supported lower performers in a math task. Mach. Learn. 113 (5) (2024)
Joy He-Yueya, Noah D. Goodman, Emma Brunskill
Evaluating and Optimizing Educational Content with Large Language Model Judgments. CoRR (2024)
Sanath Kumar Krishnamurthy, Susan Athey, Emma Brunskill
Data-driven Error Estimation: Upper Bounding Multiple Errors with No Technical Debt. CoRR (2024)
Allen Nie, Yash Chandak, Christina J. Yuan, Anirudhan Badrinath, Yannis Flet-Berliac, Emma Brunskill
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators. CoRR (2024)
Danielle R. Thomas, Jionghao Lin, Erin Gatz, Ashish Gurung, Shivang Gupta, Kole Norberg, Stephen E. Fancsali, Vincent Aleven, Lee G. Branstetter, Emma Brunskill, Kenneth R. Koedinger
Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation. LAK (2024)
Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
Experiment Planning with Function Approximation. CoRR (2024)
Alan Y. Cheng, Ellie Tanimura, Joseph Tey, Andrew C. Wu, Emma Brunskill
Brief, Just-in-Time Teaching Tips to Support Computer Science Tutors. SIGCSE (1) (2024)
Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill
Model-Based Offline Reinforcement Learning with Local Misspecification. AAAI (2023)
Danielle R. Thomas, Jionghao Lin, Erin Gatz, Ashish Gurung, Shivang Gupta, Kole Norberg, Stephen E. Fancsali, Vincent Aleven, Lee G. Branstetter, Emma Brunskill, Kenneth R. Koedinger
Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation. CoRR (2023)
Aldo Pacchiano, Jonathan Lee, Emma Brunskill
Experiment Planning with Function Approximation. NeurIPS (2023)
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill
Adaptive Instrument Design for Indirect Experiments. CoRR (2023)
Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets. NeurIPS (2023)
Allen Nie, Ann-Katrin Reuel, Emma Brunskill
Understanding the Impact of Reinforcement Learning Personalization on Subgroups of Students in Math Tutoring. AIED (Posters/Late Breaking Results/...) (2023)
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro da Silva, Emma Brunskill, Philip S. Thomas
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments. CoRR (2023)
Vincent Aleven, Richard G. Baraniuk, Emma Brunskill, Scott Crossley, Dora Demszky, Stephen Fancsali, Shivang Gupta, Kenneth R. Koedinger, Chris Piech, Steven Ritter, Danielle R. Thomas, Simon Woodhead, Wanli Xing
Towards the Future of AI-Augmented Human Tutoring in Math Learning. AIED (Posters/Late Breaking Results/...) (2023)
Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. CoRR (2023)
Sherry Ruan, Allen Nie, William Steenbergen, Jiayu He, JQ Zhang, Meng Guo, Yao Liu, Kyle Dang Nguyen, Catherine Y. Wang, Rui Ying, James A. Landay, Emma Brunskill
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task. CoRR (2023)
Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR (2023)
Aishwarya Mandyam, Matthew Jörke, Barbara E. Engelhardt, Emma Brunskill
Adaptive Interventions with User-Defined Goals for Health Behavior Change. CoRR (2023)
Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets. CoRR (2023)
Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS (2023)
Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. NeurIPS (2023)
Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill
Model-based Offline Reinforcement Learning with Local Misspecification. CoRR (2023)
Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR (2023)
Yao Liu, Yannis Flet-Berliac, Emma Brunskill
Offline policy optimization with eligible actions. UAI (2022)
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas
Off-Policy Evaluation for Action-Dependent Non-stationary Environments. NeurIPS (2022)
Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn
Giving Feedback on Interactive Student Programs with Meta-Exploration. NeurIPS (2022)
Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill
Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning. AAAI (2022)
Yao Liu, Yannis Flet-Berliac, Emma Brunskill
Offline Policy Optimization with Eligible Actions. CoRR (2022)
Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. NeurIPS (2022)
Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. CoRR (2022)
Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS (2022)
Tong Mu, Yash Chandak, Tatsunori B. Hashimoto, Emma Brunskill
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. NeurIPS (2022)
Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn
Giving Feedback on Interactive Student Programs with Meta-Exploration. CoRR (2022)
Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR (2022)
Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CHIL (2022)
Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill
Design of Experiments for Stochastic Contextual Linear Bandits. CoRR (2021)
Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CoRR (2021)
Tong Mu, Shuhan Wang, Erik Andersen, Emma Brunskill
Automatic Adaptive Sequencing in a Webgame. ITS (2021)
Andrea Zanette, Martin J. Wainwright, Emma Brunskill
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. CoRR (2021)
Allen Nie, Emma Brunskill, Chris Piech
Play to Grade: Testing Coding Games as Classifying Markov Decision Process. NeurIPS (2021)
Alex Chohlas-Wood, Madison Coots, Emma Brunskill, Sharad Goel
Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making. CoRR (2021)
Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas
Universal Off-Policy Evaluation. CoRR (2021)
Sherry Ruan, Liwei Jiang, Qianyao Xu, Zhiyuan Liu, Glenn M. Davis, Emma Brunskill, James A. Landay
EnglishBot: An AI-Powered Conversational System for Second Language Learning. IUI (2021)
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.
On the Opportunities and Risks of Foundation Models. CoRR (2021)
Hyunji Alex Nam, Scott L. Fleming, Emma Brunskill
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes. NeurIPS (2021)
Allen Nie, Emma Brunskill, Chris Piech
Play to Grade: Testing Coding Games as Classifying Markov Decision Process. CoRR (2021)
Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill
Design of Experiments for Stochastic Contextual Linear Bandits. NeurIPS (2021)
Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez
Power Constrained Bandits. MLHC (2021)
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS (2021)
Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas
Universal Off-Policy Evaluation. NeurIPS (2021)
Andrea Zanette, Martin J. Wainwright, Emma Brunskill
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. NeurIPS (2021)
Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning. CoRR (2021)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Learning Near Optimal Policies with Low Inherent Bellman Error. ICML (2020)
Sherry Ruan, Jiayu He, Rui Ying, Jonathan Burkle, Dunia Hakim, Anna Wang, Yufeng Yin, Lily Zhou, Qianyao Xu, Abdallah A. AbuHashem, Griffin Dietz, Elizabeth L. Murnane, Emma Brunskill, James A. Landay
Supporting children's math learning with feedback-augmented narrative technology. IDC (2020)
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. NeurIPS (2020)
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. NeurIPS (2020)
Ramtin Keramati, Emma Brunskill
Value Driven Representation for Human-in-the-Loop Reinforcement Learning. CoRR (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. CoRR (2020)
Weihao Kong, Emma Brunskill, Gregory Valiant
Sublinear Optimal Policy Value Estimation in Contextual Bandits. AISTATS (2020)
Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez
Power-Constrained Bandits. CoRR (2020)
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
Provably Good Batch Reinforcement Learning Without Great Exploration. CoRR (2020)
Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer. CoRR (2020)
Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. CoRR (2020)
Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS (2020)
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. CoRR (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. NeurIPS (2020)
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. CoRR (2020)
Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. ICML (2020)
Tong Mu, Andrea Jetten, Emma Brunskill
Towards Suggesting Actionable Interventions for Wheel Spinning Students. EDM (2020)
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR (2020)
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. ICML (2020)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Learning Near Optimal Policies with Low Inherent Bellman Error. CoRR (2020)
Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. AAAI (2020)
Shayan Doroudi, Ece Kamar, Emma Brunskill
Not Everyone Writes Good Examples but Good Examples Can Come from Anywhere. HCOMP (2019)
Shayan Doroudi, Emma Brunskill
Fairer but Not Fair Enough On the Equitability of Knowledge Tracing. LAK (2019)
Sherry Ruan, Liwei Jiang, Justin Xu, Bryce Joe-Kun Tham, Zhengneng Qiu, Yeshuang Zhu, Elizabeth L. Murnane, Emma Brunskill, James A. Landay
QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge. CHI (2019)
Zhaohan Daniel Guo, Emma Brunskill
Directed Exploration for Reinforcement Learning. CoRR (2019)
Andrea Zanette, Emma Brunskill
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. CoRR (2019)
Tong Mu, Karan Goel, Emma Brunskill
PLOTS: Procedure Learning from Observations using Subtask Structure. CoRR (2019)
Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. NeurIPS (2019)
Sherry Ruan, Angelica Willis, Qianyao Xu, Glenn M. Davis, Liwei Jiang, Emma Brunskill, James A. Landay
BookBuddy: Turning Digital Materials Into Interactive Foreign Language Lessons Through a Voice Chatbot. L@S (2019)
Andrea Zanette, Emma Brunskill
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. ICML (2019)
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Limiting Extrapolation in Linear Approximate Value Iteration. NeurIPS (2019)
Jonathan Bragg, Emma Brunskill
Fake It Till You Make It: Learning-Compatible Performance Support. UAI (2019)
Angelica Willis, Glenn M. Davis, Sherry Ruan, Lakshmi Manoharan, James A. Landay, Emma Brunskill
Key Phrase Extraction for Generating Educational Question-Answer Pairs. L@S (2019)
Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan, Daisy Yi Ding, Saurabh Gombar, Nigam Shah, Emma Brunskill
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare. CoRR (2019)
Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez
Combining parametric and nonparametric models for off-policy evaluation. ICML (2019)
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
Off-Policy Policy Gradient with Stationary Distribution Correction. UAI (2019)
Karan Goel, Emma Brunskill
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure. ICLR (Poster) (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Emma Brunskill