Timezone: »
Author Information
Jonathan Lee (Stanford University)
Weihao Kong (University of Washington)
Aldo Pacchiano (UC Berkeley)
Vidya Muthukumar (Georgia Institute of Technology)
Emma Brunskill (Stanford University)

Emma Brunskill is an associate tenured professor in the Computer Science Department at Stanford University. Brunskill’s lab aims to create AI systems that learn from few samples to robustly make good decisions and is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. Brunskill has received a NSF CAREER award, Office of Naval Research Young Investigator Award, a Microsoft Faculty Fellow award and an alumni impact award from the computer science and engineering department at the University of Washington. Brunskill and her lab have received multiple best paper nominations and awards both for their AI and machine learning work (UAI best paper, Reinforcement Learning and Decision Making Symposium best paper twice) and for their work in Ai of education (Intelligent Tutoring Systems Conference, Educational Data Mining conference x3, CHI).
More from the Same Authors
-
2021 : Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation »
Ke Wang · Vidya Muthukumar · Christos Thrampoulidis -
2021 : Classification and Adversarial Examples in an Overparameterized Linear Model: A Signal-Processing Perspective »
Adhyyan Narang · Vidya Muthukumar · Anant Sahai -
2021 : Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity »
Dhruv Malik · Aldo Pacchiano · Vishwak Srinivasan · Yuanzhi Li -
2021 : Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection »
Matteo Papini · Andrea Tirinzoni · Aldo Pacchiano · Marcello Restelli · Alessandro Lazaric · Matteo Pirotta -
2021 : Model-based Offline Reinforcement Learning with Local Misspecification »
Kefan Dong · Ramtin Keramati · Emma Brunskill -
2021 : Meta Learning MDPs with linear transition models »
Robert Müller · Aldo Pacchiano · Jack Parker-Holder -
2021 : Avoiding Overfitting to the Importance Weights in Offline Policy Optimization »
Yao Liu · Emma Brunskill -
2021 : Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning »
Andrea Zanette · Martin Wainwright · Emma Brunskill -
2021 : On the Theory of Reinforcement Learning with Once-per-Episode Feedback »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2022 : Giving Complex Feedback in Online Student Learning with Meta-Exploration »
Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn -
2022 : Giving Feedback on Interactive Student Programs with Meta-Exploration »
Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn -
2023 Poster: Efficient List-Decodable Regression using Batches »
Abhimanyu Das · Ayush Jain · Weihao Kong · Rajat Sen -
2023 Poster: Leveraging Offline Data in Online Reinforcement Learning »
Andrew Wagenmaker · Aldo Pacchiano -
2022 : Giving Complex Feedback in Online Student Learning with Meta-Exploration »
Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn -
2022 Poster: Universal and data-adaptive algorithms for model selection in linear contextual bandits »
Vidya Muthukumar · Akshay Krishnamurthy -
2022 Spotlight: Universal and data-adaptive algorithms for model selection in linear contextual bandits »
Vidya Muthukumar · Akshay Krishnamurthy -
2022 Poster: Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback »
Tianyi Lin · Aldo Pacchiano · Yaodong Yu · Michael Jordan -
2022 Spotlight: Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback »
Tianyi Lin · Aldo Pacchiano · Yaodong Yu · Michael Jordan -
2022 : Invited Talk: Emma Brunskill »
Emma Brunskill -
2021 : Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning »
Andrea Zanette · Martin Wainwright · Emma Brunskill -
2021 : On the Theory of Reinforcement Learning with Once-per-Episode Feedback »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2021 Poster: Defense against backdoor attacks via robust covariance estimation »
Jonathan Hayase · Weihao Kong · Raghav Somani · Sewoong Oh -
2021 Spotlight: Defense against backdoor attacks via robust covariance estimation »
Jonathan Hayase · Weihao Kong · Raghav Somani · Sewoong Oh -
2021 Poster: Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity »
Dhruv Malik · Aldo Pacchiano · Vishwak Srinivasan · Yuanzhi Li -
2021 Poster: Dynamic Balancing for Model Selection in Bandits and RL »
Ashok Cutkosky · Christoph Dann · Abhimanyu Das · Claudio Gentile · Aldo Pacchiano · Manish Purohit -
2021 Spotlight: Dynamic Balancing for Model Selection in Bandits and RL »
Ashok Cutkosky · Christoph Dann · Abhimanyu Das · Claudio Gentile · Aldo Pacchiano · Manish Purohit -
2021 Spotlight: Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity »
Dhruv Malik · Aldo Pacchiano · Vishwak Srinivasan · Yuanzhi Li -
2020 Workshop: Theoretical Foundations of Reinforcement Learning »
Emma Brunskill · Thodoris Lykouris · Max Simchowitz · Wen Sun · Mengdi Wang -
2020 Poster: On Thompson Sampling with Langevin Algorithms »
Eric Mazumdar · Aldo Pacchiano · Yian Ma · Michael Jordan · Peter Bartlett -
2020 Poster: Accelerated Message Passing for Entropy-Regularized MAP Inference »
Jonathan Lee · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2020 Poster: Stochastic Flows and Geometric Optimization on the Orthogonal Group »
Krzysztof Choromanski · David Cheikhi · Jared Quincy Davis · Valerii Likhosherstov · Achille Nazaret · Achraf Bahamou · Xingyou Song · Mrugank Akarte · Jack Parker-Holder · Jacob Bergquist · Yuan Gao · Aldo Pacchiano · Tamas Sarlos · Adrian Weller · Vikas Sindhwani -
2020 Poster: 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 -
2020 Poster: Learning Near Optimal Policies with Low Inherent Bellman Error »
Andrea Zanette · Alessandro Lazaric · Mykel Kochenderfer · Emma Brunskill -
2020 Poster: Learning to Score Behaviors for Guided Policy Optimization »
Aldo Pacchiano · Jack Parker-Holder · Yunhao Tang · Krzysztof Choromanski · Anna Choromanska · Michael Jordan -
2020 Poster: Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling »
Yao Liu · Pierre-Luc Bacon · Emma Brunskill -
2020 Poster: Ready Policy One: World Building Through Active Learning »
Philip Ball · Jack Parker-Holder · Aldo Pacchiano · Krzysztof Choromanski · Stephen Roberts -
2020 Poster: Meta-learning for Mixed Linear Regression »
Weihao Kong · Raghav Somani · Zhao Song · Sham Kakade · Sewoong Oh -
2019 Workshop: Exploration in Reinforcement Learning Workshop »
Benjamin Eysenbach · Benjamin Eysenbach · Surya Bhupatiraju · Shixiang Gu · Harrison Edwards · Martha White · Pierre-Yves Oudeyer · Kenneth Stanley · Emma Brunskill -
2019 : Emma Brunskill (Stanford) - Minimizing & Understanding the Data Needed to Learn to Make Good Sequences of Decisions »
Emma Brunskill -
2019 : panel discussion with Craig Boutilier (Google Research), Emma Brunskill (Stanford), Chelsea Finn (Google Brain, Stanford, UC Berkeley), Mohammad Ghavamzadeh (Facebook AI), John Langford (Microsoft Research) and David Silver (Deepmind) »
Peter Stone · Craig Boutilier · Emma Brunskill · Chelsea Finn · John Langford · David Silver · Mohammad Ghavamzadeh -
2019 Poster: Combining parametric and nonparametric models for off-policy evaluation »
Omer Gottesman · Yao Liu · Scott Sussex · Emma Brunskill · Finale Doshi-Velez -
2019 Oral: Combining parametric and nonparametric models for off-policy evaluation »
Omer Gottesman · Yao Liu · Scott Sussex · Emma Brunskill · Finale Doshi-Velez -
2019 Poster: Policy Certificates: Towards Accountable Reinforcement Learning »
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill -
2019 Poster: Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds »
Andrea Zanette · Emma Brunskill -
2019 Poster: Online learning with kernel losses »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett -
2019 Poster: Separable value functions across time-scales »
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill -
2019 Oral: Policy Certificates: Towards Accountable Reinforcement Learning »
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill -
2019 Oral: Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds »
Andrea Zanette · Emma Brunskill -
2019 Oral: Separable value functions across time-scales »
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill -
2019 Oral: Online learning with kernel losses »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett -
2018 Poster: Decoupling Gradient-Like Learning Rules from Representations »
Philip Thomas · Christoph Dann · Emma Brunskill -
2018 Oral: Decoupling Gradient-Like Learning Rules from Representations »
Philip Thomas · Christoph Dann · Emma Brunskill -
2018 Poster: Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs »
Andrea Zanette · Emma Brunskill -
2018 Oral: Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs »
Andrea Zanette · Emma Brunskill