NeurIPS 2019 Workshop on Machine Learning with Guarantees

Saturday, December 14, Vancouver Convention Center, BC, Canada

Schedule

08:50 - 09:00 Welcome address: Ben London
09:00 - 09:45 Invited talk: Tengyu Ma, "Designing Explicit Regularizers for Deep Models"
09:45 - 10:15 Contributed talk: Vatsal Sharan, "Sample Amplification: Increasing Dataset Size even when Learning is Impossible"
10:15 - 10:45 Coffee break / poster session 1
10:45 - 11:30 Invited talk: Mehryar Mohri, "Learning with Sample-Dependent Hypothesis Sets"
11:30 - 12:00 Contributed talk: James Lucas, "Information-theoretic limitations on novel task generalization"
12:00 - 13:45 Lunch on your own
13:45 - 14:30 Invited talk: Soheil Feizi, "Certifiable Defenses against Adversarial Attacks"
14:30 - 15:00 Contributed talk: Maksym Andriushchenko, "Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks"
15:00 - 15:30 Coffee break / poster session 2
15:30 - 16:15 Invited talk: Aaron Roth, "Average Individual Fairness"
16:15 - 16:45 Contributed talk: Hussein Mozannar, "Fair Learning with Private Data"
16:45 - 17:30 Invited talk: Emma Brunskill, "Some Theory RL Challenges Inspired by Education"
17:30 - 18:00 Panel discussion

Posters will be up all day. There will be two official poster sessions during the coffee breaks.