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Author Information
Andrea Zanette (Stanford University)
Alessandro Lazaric (Facebook AI Research)
Mykel Kochenderfer (Stanford University)
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
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2021 : Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection »
Matteo Papini · Andrea Tirinzoni · Aldo Pacchiano · Marcello Restelli · Alessandro Lazaric · Matteo Pirotta -
2021 : A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs »
Andrea Tirinzoni · Matteo Pirotta · Alessandro Lazaric -
2021 : Model-based Offline Reinforcement Learning with Local Misspecification »
Kefan Dong · Ramtin Keramati · Emma Brunskill -
2021 : Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret »
Jean Tarbouriech · Jean Tarbouriech · Simon Du · Matteo Pirotta · Michal Valko · Alessandro Lazaric -
2021 : A general sample complexity analysis of vanilla policy gradient »
Rui Yuan · Robert Gower · Alessandro Lazaric -
2021 : Estimating Optimal Policy Value in Linear Contextual Bandits beyond Gaussianity »
Jonathan Lee · Weihao Kong · Aldo Pacchiano · Vidya Muthukumar · Emma Brunskill -
2021 : Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching »
Pierre-Alexandre Kamienny · Jean Tarbouriech · Alessandro Lazaric · Ludovic Denoyer -
2021 : Exploration-Driven Representation Learning in Reinforcement Learning »
Akram Erraqabi · Mingde Zhao · Marlos C. Machado · Yoshua Bengio · Sainbayar Sukhbaatar · Ludovic Denoyer · Alessandro Lazaric -
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 -
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 : Experiment Planning with Function Approximation »
Aldo Pacchiano · Jonathan Lee · Emma Brunskill -
2023 : In-Context Decision-Making from Supervised Pretraining »
Jonathan Lee · Annie Xie · Aldo Pacchiano · Yash Chandak · Chelsea Finn · Ofir Nachum · Emma Brunskill -
2023 : Experiment Planning with Function Approximation »
Aldo Pacchiano · Jonathan Lee · Emma Brunskill -
2023 Poster: Layered State Discovery for Incremental Autonomous Exploration »
Liyu Chen · Andrea Tirinzoni · Alessandro Lazaric · Matteo Pirotta -
2023 Panel: ICML Education Outreach Panel »
Andreas Krause · Barbara Engelhardt · Emma Brunskill · Kyunghyun Cho -
2022 : Giving Complex Feedback in Online Student Learning with Meta-Exploration »
Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn -
2022 Workshop: Responsible Decision Making in Dynamic Environments »
Virginie Do · Thorsten Joachims · Alessandro Lazaric · Joelle Pineau · Matteo Pirotta · Harsh Satija · Nicolas Usunier -
2022 Poster: Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times »
Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco -
2022 Spotlight: Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times »
Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco -
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 : Invited Talk by Alessandro Lazaric »
Alessandro Lazaric -
2021 Poster: Leveraging Good Representations in Linear Contextual Bandits »
Matteo Papini · Andrea Tirinzoni · Marcello Restelli · Alessandro Lazaric · Matteo Pirotta -
2021 Spotlight: Leveraging Good Representations in Linear Contextual Bandits »
Matteo Papini · Andrea Tirinzoni · Marcello Restelli · Alessandro Lazaric · Matteo Pirotta -
2021 Poster: Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL »
Andrea Zanette -
2021 Oral: Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL »
Andrea Zanette -
2021 Poster: Reinforcement Learning with Prototypical Representations »
Denis Yarats · Rob Fergus · Alessandro Lazaric · Lerrel Pinto -
2021 Spotlight: Reinforcement Learning with Prototypical Representations »
Denis Yarats · Rob Fergus · Alessandro Lazaric · Lerrel Pinto -
2020 Workshop: Theoretical Foundations of Reinforcement Learning »
Emma Brunskill · Thodoris Lykouris · Max Simchowitz · Wen Sun · Mengdi Wang -
2020 Poster: No-Regret Exploration in Goal-Oriented Reinforcement Learning »
Jean Tarbouriech · Evrard Garcelon · Michal Valko · Matteo Pirotta · Alessandro Lazaric -
2020 Poster: Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation »
Marc Abeille · Alessandro Lazaric -
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: Meta-learning with Stochastic Linear Bandits »
Leonardo Cella · Alessandro Lazaric · Massimiliano Pontil -
2020 Poster: Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling »
Yao Liu · Pierre-Luc Bacon · Emma Brunskill -
2020 Poster: Near-linear time Gaussian process optimization with adaptive batching and resparsification »
Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco -
2020 Poster: Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM »
Kunal Menda · Jean de Becdelievre · Jayesh K. Gupta · Ilan Kroo · Mykel Kochenderfer · Zachary Manchester -
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: 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 -
2018 Poster: Decoupling Gradient-Like Learning Rules from Representations »
Philip Thomas · Christoph Dann · Emma Brunskill -
2018 Poster: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2018 Oral: Decoupling Gradient-Like Learning Rules from Representations »
Philip Thomas · Christoph Dann · Emma Brunskill -
2018 Oral: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2018 Poster: Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning »
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner -
2018 Poster: Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems »
Marc Abeille · Alessandro Lazaric -
2018 Poster: Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs »
Andrea Zanette · Emma Brunskill -
2018 Oral: Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems »
Marc Abeille · Alessandro Lazaric -
2018 Oral: Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning »
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner -
2018 Oral: Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs »
Andrea Zanette · Emma Brunskill