electrical circuits in the shape of a brain
June 22, 2020 – June 23, 2020

Trustworthy and Robust AI Collaboration (TRAC) Workshop

9:30 AM–1:00 PM PDT

Location: Virtual

Each day’s agenda starts at 9:30am PDT sharp.

Monday, June 22, 2020

Time (PDT) Session Speaker / Talk Title
9:30-9:35 Welcome and Introduction Aleksander Mądry (opens in new tab), Massachusetts Institute of Technology and Evelyne Viegas, Microsoft

Eric Horvitz, Microsoft and Daniela Rus (opens in new tab), Massachusetts Institute of Technology
9:35-10:10 Opening Talk Constantinos Daskalakis (opens in new tab), Massachusetts Institute of Technology
Robust Learning from Censored Data

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10:10-10:15 Break
10:15-11:05 Lightning Talks Tamara Broderick (opens in new tab), Massachusetts Institute of Technology
Approximate Cross-Validation for Complex Models

Ankur Moitra (opens in new tab), Massachusetts Institute of Technology
Classification under Misspecification, and Implications for Fairness

Maggie Makar (opens in new tab), Massachusetts Institute of Technology
Learnability of Contagious Infections Under Incomplete Testing

Kai Jia (opens in new tab), Massachusetts Institute of Technology
Exploiting Verified Neural Networks via Floating Point Numerical Error

Lucas Liebenwein (opens in new tab), Massachusetts Institute of Technology
Provable Filter Pruning for Efficient Neural Networks

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11:05-11:15 Break
11:15-12:15 Breakout Sessions Select One:

  • Real-world ML Safety and Reliability
  • Coping with Biases in Data, with a Focus on ML for Healthcare (including the COVID-19-related efforts)
12:15-12:30 Break
12:30-1:00 Report Out Watch this session on-demand

Tuesday, June 23, 2020

Time (PDT) Session Speaker / Talk Title
9:30-9:35 Welcome and Introduction Aleksander Mądry (opens in new tab), Massachusetts Institute of Technology and Evelyne Viegas, Microsoft

Eric Horvitz, Microsoft and Daniela Rus (opens in new tab), Massachusetts Institute of Technology
9:35-10:10 Opening Talk Hal Daumé III, Microsoft
Language (technology) is Power

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10:10-10:15 Break
10:15-11:05 Lightning Talks Yael Kalai, Microsoft
Learning with Arbitrary Adversarial Test Examples

Stefanie Jegelka (opens in new tab), Massachusetts Institute of Technology
Unsupervised Risk Estimation with Domain-Invariant Predictors

Hadi Salman (opens in new tab), Microsoft
Do Adversarially Robust ImageNet Models Transfer Better?

Pouya Hamadanian (opens in new tab), Massachusetts Institute of Technology
Towards Safe Online Reinforcement Learning in Computer Systems

Cathy Wu (opens in new tab), Massachusetts Institute of Technology
Policy transfer across networks: towards understanding AI impacts in urban-scale systems

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11:05-11:15 Break
11:15-12:15 Breakout Sessions Select One:

  • Human-ML Interface: Interpretability/Explainability
  • Adversarially Robust ML
12:15-12:30 Break
12:30-12:50 Report Out
12:50-1:00 Closing Remarks and Next Steps Eric Horvitz, Microsoft and Daniela Rus (opens in new tab), Massachusetts Institute of Technology

Aleksander Mądry (opens in new tab), Massachusetts Institute of Technology and Evelyne Viegas, Microsoft

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