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ORCIDEmma Brunskill
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- affiliation: Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2023
[i67]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)- [i66]Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Model-based Offline Reinforcement Learning with Local Misspecification. CoRR abs/2301.11426 (2023) - [i65]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) - [i64]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) - 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 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]Zhaohan Daniel Guo, Emma Brunskill:
Directed Exploration for Reinforcement Learning. CoRR abs/1906.07805 (2019) - [i35]Yao Liu, Pierre-Luc Bacon, Emma Brunskill:
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. CoRR abs/1910.06508 (2019) - [i34]Andrea Zanette, Emma Brunskill:
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. CoRR abs/1911.00954 (2019) - [i33]Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill:
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. CoRR abs/1911.01546 (2019) - [i32]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 abs/1911.07084 (2019) - [i31]Weihao Kong, Gregory Valiant, Emma Brunskill:
Sublinear Optimal Policy Value Estimation in Contextual Bandits. CoRR abs/1912.06111 (2019) - 2018
- [c75]Philip S. Thomas, Christoph Dann, Emma Brunskill:
Decoupling Gradient-Like Learning Rules from Representations. ICML 2018: 4924-4932 - [c74]Andrea Zanette, Emma Brunskill:
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. ICML 2018: 5732-5740 - [c73]Shayan Doroudi, Philip S. Thomas, Emma Brunskill:
Importance Sampling for Fair Policy Selection. IJCAI 2018: 5239-5243 - [c72]Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar:
Efficient Exploration Through Bayesian Deep Q-Networks. ITA 2018: 1-9 - [c71]Tong Mu, Shuhan Wang, Erik Andersen, Emma Brunskill:
Combining adaptivity with progression ordering for intelligent tutoring systems. L@S 2018: 15:1-15:4 - [c70]Y. Alex Kolchinski, Sherry Ruan, Dan Schwartz, Emma Brunskill:
Adaptive natural-language targeting for student feedback. L@S 2018: 26:1-26:4 - [c69]Emma Brunskill, Dawn Zimmaro, Candace Thille:
Exploring the impact of the default option on student engagement and performance in a statistics MOOC. L@S 2018: 34:1-34:4 - [c68]Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-policy Policy Evaluation. NeurIPS 2018: 2649-2658 - [c67]Sharon Zhou, Tong Mu, Karan Goel, Michael S. Bernstein, Emma Brunskill:
Shared Autonomy for an Interactive AI System. UIST (Adjunct Volume) 2018: 20-22 - [i30]Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar:
Efficient Exploration through Bayesian Deep Q-Networks. CoRR abs/1802.04412 (2018) - [i29]Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-Policy Policy Evaluation. CoRR abs/1805.09044 (2018) - [i28]Yao Liu, Emma Brunskill:
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms. CoRR abs/1805.09045 (2018) - [i27]Ramtin Keramati, Jay Whang, Patrick Cho, Emma Brunskill:
Strategic Object Oriented Reinforcement Learning. CoRR abs/1806.00175 (2018) - [i26]Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill, Zachary C. Lipton, Animashree Anandkumar:
Sample-Efficient Deep RL with Generative Adversarial Tree Search. CoRR abs/1806.05780 (2018) - [i25]Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill:
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. CoRR abs/1807.01066 (2018) - [i24]Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill:
Policy Certificates: Towards Accountable Reinforcement Learning. CoRR abs/1811.03056 (2018) - [i23]Peter Henderson, Emma Brunskill:
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research. CoRR abs/1812.01074 (2018) - 2017
- [c66]Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic:
Where to Add Actions in Human-in-the-Loop Reinforcement Learning. AAAI 2017: 2322-2328 - [c65]Philip S. Thomas, Emma Brunskill:
Importance Sampling with Unequal Support. AAAI 2017: 2646-2652 - [c64]Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, Emma Brunskill:
Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. AAAI 2017: 4740-4745 - [c63]Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu:
Trading off Rewards and Errors in Multi-Armed Bandits. AISTATS 2017: 709-717 - [c62]Shayan Doroudi, Emma Brunskill:
The Misidentified Identifiability Problem of Bayesian Knowledge Tracing. EDM 2017 - [c61]Karan Goel, Christoph Dann, Emma Brunskill:
Sample Efficient Policy Search for Optimal Stopping Domains. IJCAI 2017: 1711-1717 - [c60]Shayan Doroudi, Vincent Aleven, Emma Brunskill:
Robust Evaluation Matrix: Towards a More Principled Offline Exploration of Instructional Policies. L@S 2017: 3-12 - [c59]Zhaohan Guo, Philip S. Thomas, Emma Brunskill:
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation. NIPS 2017: 2492-2501 - [c58]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill:
Regret Minimization in MDPs with Options without Prior Knowledge. NIPS 2017: 3166-3176 - [c57]Christoph Dann, Tor Lattimore, Emma Brunskill:
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. NIPS 2017: 5713-5723 - [c56]Shayan Doroudi, Philip S. Thomas, Emma Brunskill:
Importance Sampling for Fair Policy Selection. UAI 2017 - [i22]Karan Goel, Christoph Dann, Emma Brunskill:
Sample Efficient Policy Search for Optimal Stopping Domains. CoRR abs/1702.06238 (2017) - [i21]Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill:
Using Options for Long-Horizon Off-Policy Evaluation. CoRR abs/1703.03453 (2017) - [i20]Zhaohan Daniel Guo, Emma Brunskill:
Sample Efficient Feature Selection for Factored MDPs. CoRR abs/1703.03454 (2017) - [i19]Christoph Dann, Tor Lattimore, Emma Brunskill:
UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. CoRR abs/1703.07710 (2017) - [i18]Philip S. Thomas, Christoph Dann, Emma Brunskill:
Decoupling Learning Rules from Representations. CoRR abs/1706.03100 (2017) - [i17]Philip S. Thomas, Emma Brunskill:
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines. CoRR abs/1706.06643 (2017) - [i16]Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Emma Brunskill:
On Ensuring that Intelligent Machines Are Well-Behaved. CoRR abs/1708.05448 (2017) - [i15]Thomas Kollar, Stefanie Tellex, Matthew R. Walter, Albert Huang, Abraham Bachrach, Sachithra Hemachandra, Emma Brunskill, Ashis Gopal Banerjee, Deb Roy, Seth J. Teller, Nicholas Roy:
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands. CoRR abs/1712.01097 (2017) - 2016
- [j6]Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto:
Faster Teaching via POMDP Planning. Cogn. Sci. 40(6): 1290-1332 (2016) - [c55]Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic:
Offline Evaluation of Online Reinforcement Learning Algorithms. AAAI 2016: 1926-1933 - [c54]Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill:
A PAC RL Algorithm for Episodic POMDPs. AISTATS 2016: 510-518 - [c53]Yao Liu, Zhaohan Guo, Emma Brunskill:
PAC Continuous State Online Multitask Reinforcement Learning with Identification. AAMAS 2016: 438-446 - [c52]Shayan Doroudi, Ece Kamar, Emma Brunskill, Eric Horvitz:
Toward a Learning Science for Complex Crowdsourcing Tasks. CHI 2016: 2623-2634 - [c51]James Derek Lomas, Jodi Forlizzi, Nikhil Poonwala, Nirmal Patel, Sharan Shodhan, Kishan Patel, Kenneth R. Koedinger, Emma Brunskill:
Interface Design Optimization as a Multi-Armed Bandit Problem. CHI 2016: 4142-4153 - [c50]