Offline Reinforcement Learning Workshop

Neural Information Processing Systems (NeurIPS)

December 12, 2020

@OfflineRL · #OFFLINERL2020

Panel Questions: You can submit questions for the live panel discussion using this Google form.

Invited Speakers

Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University. Her goal is to create AI systems that learn from few samples to robustly make good decisions, motivated by applications to healthcare and education.
Finale Doshi-Velez is an associate professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. Her interests lie at the intersection of machine learning, healthcare, and interpretablity.
John Langford is a partner researcher in the Machine Learning group at Microsoft Research NYC, is a reinforcement learning expert who is working, in his own words, to solve machine learning. He serves as the President of ICML from 2019–2021.
Nan Jiang is an assistant professor at UIUC. His main interests include improving sample efficiency of reinforcement learning (RL), and using ideas from statistical learning theory to analyze and develop RL algorithms.
Brandyn White is a Staff Software Engineer at Waymo Research where he works on realistic multi-agent simulation for self-driving.
Nando De Freitas is a principal scientist at DeepMind and a CIFAR Fellow. He was previously a full professor at UBC and University of Oxford. In his own words, he researches intelligence to understand what we are, and to harness it wisely.

Invited Panelists

In addition to the speakers above, we have additional invited panelists.

Sergey Levine is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. In his research, he focuses on the intersection between control and machine learning, with the aim of developing algorithms and techniques that can endow machines with the ability to autonomously acquire the skills for executing complex tasks.