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ORCIDEmma Brunskill
Emma P. Brunskill
Person information
- affiliation: Stanford University, CA, USA
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2024
[j9]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): 3023-3048 (2024)- [j8]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) - [c137]Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Pontes Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya J. Shah, Thomas Savage, Ethan Goh, Akshay S. Chaudhari, Nima Aghaeepour, Christopher D. Sharp, Michael A. Pfeffer, Percy Liang, Jonathan H. Chen, Keith E. Morse, Emma P. Brunskill, Jason A. Fries, Nigam H. Shah:
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records. AAAI 2024: 22021-22030 - [c136]Joy He-Yueya, Noah D. Goodman, Emma Brunskill:
Evaluating and Optimizing Educational Content with Large Language Model Judgments. EDM 2024 - [c135]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. EMNLP 2024: 10570-10603 - [c134]Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill:
Adaptive Instrument Design for Indirect Experiments. ICLR 2024 - [c133]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: 404-415 - [c132]Amelia Leon, Allen Nie, Yash Chandak, Emma Brunskill:
Estimating the Causal Treatment Effect of Unproductive Persistence. LAK 2024: 843-849 - [c131]Emma Brunskill, Kole A. Norberg, Stephen Fancsali, Steven Ritter:
Examining the Use of an AI-Powered Teacher Orchestration Tool at Scale. L@S 2024: 356-360 - [c130]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: 200-206 - [c129]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: 736-742 - [c128]Yuchen Hu, Henry Zhu, Emma Brunskill, Stefan Wager:
Minimax-Regret Sample Selection in Randomized Experiments. EC 2024: 1209-1235 - [i83]Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill:
Experiment Planning with Function Approximation. CoRR abs/2401.05193 (2024) - [i82]Joy He-Yueya, Noah D. Goodman, Emma Brunskill:
Evaluating and Optimizing Educational Content with Large Language Model Judgments. CoRR abs/2403.02795 (2024) - [i81]Sanath Kumar Krishnamurthy, Susan Athey, Emma Brunskill:
Data-driven Error Estimation: Upper Bounding Multiple Errors with No Technical Debt. CoRR abs/2405.04636 (2024) - [i80]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 abs/2405.06061 (2024) - [i79]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 abs/2405.17708 (2024) - [i78]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 abs/2407.00870 (2024) - [i77]Hyunji Alex Nam, Yash Chandak, Emma Brunskill:
Short-Long Policy Evaluation with Novel Actions. CoRR abs/2407.03674 (2024) - [i76]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 abs/2407.09975 (2024) - [i75]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 abs/2407.15645 (2024) - 2023
- [c127]Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Model-Based Offline Reinforcement Learning with Local Misspecification. AAAI 2023: 7423-7431 - [c126]Vincent Aleven, Richard G. Baraniuk, Emma Brunskill, Scott A. 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: 26-31 - [c125]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: 688-694 - [c124]Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS 2023 - [c123]Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets. NeurIPS 2023 - [c122]Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill:
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. NeurIPS 2023 - [c121]Aldo Pacchiano, Jonathan Lee, Emma Brunskill:
Experiment Planning with Function Approximation. NeurIPS 2023 - [e1]Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett:
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research 202, PMLR 2023 [contents] - [i74]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 abs/2301.10330 (2023) - [i73]Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Model-based Offline Reinforcement Learning with Local Misspecification. CoRR abs/2301.11426 (2023) - [i72]Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR abs/2302.09451 (2023) - [i71]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 abs/2304.04933 (2023) - [i70]Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets. CoRR abs/2306.14069 (2023) - [i69]Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR abs/2306.14892 (2023) - [i68]Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill:
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. CoRR abs/2307.02108 (2023) - [i67]Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Pontes Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya J. Shah, Thomas Savage, Ethan Goh, Akshay S. Chaudhari, Nima Aghaeepour, Christopher D. Sharp, Michael A. Pfeffer, Percy Liang, Jonathan H. Chen, Keith E. Morse, Emma P. Brunskill, Jason A. Fries, Nigam H. Shah:
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records. CoRR abs/2308.14089 (2023) - [i66]Aishwarya Mandyam, Matthew Jörke, Barbara E. Engelhardt, Emma Brunskill:
Adaptive Interventions with User-Defined Goals for Health Behavior Change. CoRR abs/2311.09483 (2023) - [i65]Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill:
Adaptive Instrument Design for Indirect Experiments. CoRR abs/2312.02438 (2023) - [i64]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 abs/2312.11274 (2023) - 2022
- [c120]Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill:
Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning. AAAI 2022: 7841-7849 - [c119]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: 397-410 - [c118]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 - [c117]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS 2022 - [c116]Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn:
Giving Feedback on Interactive Student Programs with Meta-Exploration. NeurIPS 2022 - [c115]Tong Mu, Yash Chandak, Tatsunori B. Hashimoto, Emma Brunskill:
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. NeurIPS 2022 - [c114]Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill:
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. NeurIPS 2022 - [c113]Yao Liu, Yannis Flet-Berliac, Emma Brunskill:
Offline policy optimization with eligible actions. UAI 2022: 1253-1263 - [i63]Yao Liu, Yannis Flet-Berliac, Emma Brunskill:
Offline Policy Optimization with Eligible Actions. CoRR abs/2207.00632 (2022) - [i62]Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill:
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. CoRR abs/2210.08642 (2022) - [i61]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR abs/2211.02016 (2022) - [i60]Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn:
Giving Feedback on Interactive Student Programs with Meta-Exploration. CoRR abs/2211.08802 (2022) - 2021
- [c112]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS 2021: 3340-3348 - [c111]Tong Mu, Shuhan Wang, Erik Andersen, Emma Brunskill:
Automatic Adaptive Sequencing in a Webgame. ITS 2021: 430-438 - [c110]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: 434-444 - [c109]Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Power Constrained Bandits. MLHC 2021: 209-259 - [c108]Allen Nie, Emma Brunskill, Chris Piech:
Play to Grade: Testing Coding Games as Classifying Markov Decision Process. NeurIPS 2021: 1506-1518 - [c107]Andrea Zanette, Martin J. Wainwright, Emma Brunskill:
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. NeurIPS 2021: 13626-13640 - [c106]Hyunji Alex Nam, Scott L. Fleming, Emma Brunskill:
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes. NeurIPS 2021: 15650-15666 - [c105]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. NeurIPS 2021: 22720-22731 - [c104]Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas:
Universal Off-Policy Evaluation. NeurIPS 2021: 27475-27490 - [i59]Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas:
Universal Off-Policy Evaluation. CoRR abs/2104.12820 (2021) - [i58]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. CoRR abs/2107.09912 (2021) - [i57]Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. 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 E. 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 abs/2108.07258 (2021) - [i56]Andrea Zanette, Martin J. Wainwright, Emma Brunskill:
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. CoRR abs/2108.08812 (2021) - [i55]Alex Chohlas-Wood, Madison Coots, Emma Brunskill, Sharad Goel:
Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making. CoRR abs/2109.08792 (2021) - [i54]Allen Nie, Emma Brunskill, Chris Piech:
Play to Grade: Testing Coding Games as Classifying Markov Decision Process. CoRR abs/2110.14615 (2021) - [i53]Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill:
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation. CoRR abs/2111.14272 (2021) - [i52]Tong Mu, Georgios Theocharous, David Arbour, Emma Brunskill:
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning. CoRR abs/2112.15221 (2021) - 2020
- [c103]Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill:
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. AAAI 2020: 4436-4443 - [c102]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: 567-580 - [c101]Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS 2020: 1954-1964 - [c100]Weihao Kong, Emma Brunskill, Gregory Valiant:
Sublinear Optimal Policy Value Estimation in Contextual Bandits. AISTATS 2020: 4377-4387 - [c99]Tong Mu, Andrea Jetten, Emma Brunskill:
Towards Suggesting Actionable Interventions for Wheel Spinning Students. EDM 2020 - [c98]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: 3658-3667 - [c97]Yao Liu, Pierre-Luc Bacon, Emma Brunskill:
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. ICML 2020: 6184-6193 - [c96]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Learning Near Optimal Policies with Low Inherent Bellman Error. ICML 2020: 10978-10989 - [c95]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. NeurIPS 2020 - [c94]Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill:
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. NeurIPS 2020 - [c93]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. NeurIPS 2020 - [i51]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 abs/2002.03478 (2020) - [i50]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 abs/2002.05651 (2020) - [i49]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Learning Near Optimal Policies with Low Inherent Bellman Error. CoRR abs/2003.00153 (2020) - [i48]Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill:
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. CoRR abs/2003.05623 (2020) - [i47]Ramtin Keramati, Emma Brunskill:
Value Driven Representation for Human-in-the-Loop Reinforcement Learning. CoRR abs/2004.01223 (2020) - [i46]Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez:
Power-Constrained Bandits. CoRR abs/2004.06230 (2020) - [i45]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 abs/2007.05896 (2020) - [i44]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Reinforcement Learning Without Great Exploration. CoRR abs/2007.08202 (2020) - [i43]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. CoRR abs/2008.07737 (2020) - [i42]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR abs/2011.09750 (2020)
2010 – 2019
- 2019
- [j7]Shayan Doroudi, Vincent Aleven, Emma Brunskill:
Where's the Reward? Int. J. Artif. Intell. Educ. 29(4): 568-620 (2019) - [c92]Tong Mu, Karan Goel, Emma Brunskill:
PLOTS: Procedure Learning from Observations using subTask Structure. AAMAS 2019: 1007-1015 - [c91]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: 357 - [c90]Shayan Doroudi, Ece Kamar, Emma Brunskill:
Not Everyone Writes Good Examples but Good Examples Can Come from Anywhere. HCOMP 2019: 12-21 - [c89]Karan Goel, Emma Brunskill:
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure. ICLR (Poster) 2019 - [c88]Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill:
Policy Certificates: Towards Accountable Reinforcement Learning. ICML 2019: 1507-1516 - [c87]Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining parametric and nonparametric models for off-policy evaluation. ICML 2019: 2366-2375 - [c86]Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill:
Separable value functions across time-scales. ICML 2019: 5468-5477 - [c85]Andrea Zanette, Emma Brunskill:
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. ICML 2019: 7304-7312 - [c84]Shayan Doroudi, Emma Brunskill:
Fairer but Not Fair Enough On the Equitability of Knowledge Tracing. LAK 2019: 335-339 - [c83]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: 20:1-20:10 - [c82]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: 30:1-30:4 - [c81]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Limiting Extrapolation in Linear Approximate Value Iteration. NeurIPS 2019: 5616-5625 - [c80]Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill:
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. NeurIPS 2019: 5626-5635 - [c79]Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas:
Offline Contextual Bandits with High Probability Fairness Guarantees. NeurIPS 2019: 14893-14904 - [c78]Jonathan Bragg, Emma Brunskill:
Fake It Till You Make It: Learning-Compatible Performance Support. UAI 2019: 915-924 - [c77]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with Stationary Distribution Correction. UAI 2019: 1180-1190 - [c76]Ramtin Keramati, Emma Brunskill:
Value Driven Representation for Human-in-the-Loop Reinforcement Learning. UMAP 2019: 176-180 - [i41]Andrea Zanette, Emma Brunskill:
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. CoRR abs/1901.00210 (2019) - [i40]Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Emma Brunskill, Joelle Pineau:
Separating value functions across time-scales. CoRR abs/1902.01883 (2019) - [i39]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with State Distribution Correction. CoRR abs/1904.08473 (2019) - [i38]Tong Mu, Karan Goel, Emma Brunskill:
PLOTS: Procedure Learning from Observations using Subtask Structure. CoRR abs/1904.09162 (2019) - [i37]Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining Parametric and Nonparametric Models for Off-Policy Evaluation. CoRR abs/1905.05787 (2019) - [i36]