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Author Information
Joshua Romoff (McGill University)
Peter Henderson (Stanford University)
Ahmed Touati (MILA / FAIR)
Yann Ollivier (Facebook Artificial Intelligence Research)
Joelle Pineau (McGill University / Facebook)
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).
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Separable value functions across time-scales »
Wed. Jun 12th 01:30 -- 04:00 AM Room Pacific Ballroom #111
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2021 : Model-based Offline Reinforcement Learning with Local Misspecification »
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2020 : Q&A: Peter Henderson »
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2020 : Invited Talk: Peter Henderson »
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2020 Workshop: Theoretical Foundations of Reinforcement Learning »
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2020 Poster: Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions »
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2019 Workshop: Generative Modeling and Model-Based Reasoning for Robotics and AI »
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