7:30am - 7:40am Opening remarks by Sharon Yixuan Li
7:40am - 8:10 Invited talk by Matthias Hein Towards reliable and robust machine learning
8:10am - 9:00am Spotlight Session
9:00am - 10:00am Poster Session
10:30am - 11:00 Invited talk by Finale Doshi-Velez Uncertainty in Deep Learning: How to be Bayesian?
11:00am - 11:30 Invited talk by Percy Liang Tradeoffs between Robustness and Accuracy
11:30am - 12:30pm Panel Discussion
1:30pm - 2:00pm Invited talk by Raquel Urtasun Uncertainty and Robustness for Self-driving
2:00pm - 2:10pm Contributed talk #1 Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
2:10pm - 2:20pm Contributed talk #2 Improving robustness against common corruptions by covariate shift adaptation
2:20pm - 2:30pm Contributed talk #3 A Unified View of Label Shift Estimation
2:30pm - 3:00pm Invited talk by Justin Gilmer Why Adversarial Examples Feel Like Bugs
3:30pm - 3:40pm Contributed talk #4 A Benchmark of Medical Out of Distribution Detection
3:40pm - 3:50pm Contributed talk #5 Neural Ensemble Search for Performant and Calibrated Predictions
3:50pm - 4:00pm Contributed talk #6 Bayesian model averaging is suboptimal for generalization under model misspecification