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Coffee and Check-In
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Opening Remarks
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Knowledge is embedded in language neural networks but can they reason?
Chris Manning (Stanford University)
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Machine Learning-based Design of Proteins and Small Molecules
Jennifer Listgarten (UC Berkeley)
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Break
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Efficient Deep Learning with Humans in the Loop
Zachary Lipton (Carnegie Mellon University)
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Lunch
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Aligning ML objectives with human values
Paul Christiano (UC Berkeley)
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Is Deeper Better Only When Shallow Is Good?
Shai Shalev-Shwartz  (The Hebrew University of Jerusalem)
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Break
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Flexible Neural Networks and the Frontiers of Meta-Learning
Chelsea Finn (Stanford University)
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Reception
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Coffee and Check-In
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On the Hardness of Reinforcement Learning With Value-function Approximation
Nan Jiang (University of Illinois Urbana-Champaign)
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Reinforcement Learning in Feature Space: Complexity and Regret
Mengdi Wang (Princeton University)
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Break
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Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
Alekh Agarwal (Microsoft Research Redmond)
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Lunch
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Off-policy Policy Optimization
Dale Schuurmans (Google Brain & University of Alberta)
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Reinforcement Learning in Recommender Systems: Some Challenges
Craig Boutilier (Google and University of Toronto)
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Break
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Panel
Lihong Li  (Google Brain), Jennifer Listgarten (UC Berkeley), Elchanan Mossel (Massachusetts Institute of Technology), Shai Shalev-Shwartz  (The Hebrew University of Jerusalem), Mengdi Wang (Princeton University), Po-Ling Loh (University of Wisconsin, Madison), Matus Telgarsky (University of Illinois, Urbana-Champaign)
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Coffee and Check-In
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Reinforcement Learning via an Optimization Lens
Lihong Li (Google Brain)
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Towards Verified Deep Learning
Sanjit Seshia (UC Berkeley)
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Break
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Integrating Constraints into Deep Learning Architectures with Structured Layers
Zico Kolter (CMU Bosch)
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Lunch
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The Measure and Mismeasure of Fairness
Sharad Goel (Stanford University)
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Procurement as Policy: Administrative Process For Machine Learning
Deirdre Mulligan (UC Berkeley)
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Break
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Panel on Ethics
Sharad Goel (Stanford University), Deirdre Mulligan (UC Berkeley), Emily Witt (Salesforce), Alice Xiang (Partnership on AI), Deirdre Mulligan (UC Berkeley), Matus Telgarsky (University of Illinois, Urbana-Champaign)
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Coffee and Check-In
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Inherent Trade-offs with the Local Explanations Paradigm
Julius Adebayo (MIT)
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How to Fail Interpretability Research
Been Kim (Google Brain)
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Lunch
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Better Learning from the Past: Counterfactual / Batch RL
Emma Brunskill (Stanford University)
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Designing Robust Learners
Jacob Steinhardt (Stanford University)