The Trustworthy and Robust AI collaboration (TRAC) between MIT CSAIL and Microsoft Research is working towards fostering advances on robustness and trustworthy AI, which spans safety & reliability, intelligibility, and accountability. The collaboration seeks to address concerns about the trustworthiness of AI systems, including rising concerns with the safety, fairness, and transparency of technologies.
The 3rd TRAC workshop will be run as a virtual event. It will feature a number of short talks providing an update on the TRAC collaborations, as well as an opportunity to do deep dives in the projects below:
- Stefanie Jegelka, ML with theoretical guarantees
- Aleksander Madry, Towards ML you can rely on
- Ankur Moitra, Robustness meets Algorithms
- Daniela Rus, Efficient and Explainable ML Algorithms using Coresets
- Tamara Broderick, Bayesian ML: uncertainty and robustness at scale
- Vinod Vaikuntanathan, Yael Kalai, Distributed, Private and Efficient Machine Learning
- Martin Rinard, Shuvendu Lahiri/Madan Musuvathi, State-Based Approaches for Verifying and Testing Neural Networks
- Mohammad Alizadeh, Siddhartha Sen, Safe Online Reinforcement Learning in Networked Systems
- Cathy Wu, Alekh Agarwal/Adith Swaminathan, Off-policy evaluation for risk-aware autonomous systems
- John Guttag, Eric Horvitz, Exploration of robust machine learning for high-stakes predictions
Information on The Trustworthy and Robust AI collaboration (TRAC) between MIT CSAIL and Microsoft Research along with past workshops can be found here.
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