Learning Augmented Joint-Space Task-Oriented Dynamical Systems: A Linear Parameter Varying and Synergetic Control Approach. - Yonadav Shavit; Nadia Figueroa; Seyed Sina Mirrazavi Salehian; Aude Billard (IEEE Robotics Autom. Lett., 2018)

Extracting Incentives from Black-Box Decisions. - Yonadav Shavit; William S. Moses (CoRR, 2019)

Causal Strategic Linear Regression. - Yonadav Shavit; Benjamin L. Edelman; Brian Axelrod (ICML, 2020)

Learning From Strategic Agents: Accuracy, Improvement, and Causality. - Yonadav Shavit; Benjamin L. Edelman; Brian Axelrod (CoRR, 2020)

Strengthening Subcommunities: Towards Sustainable Growth in AI Research. - Andi Peng; Jessica Zosa Forde; Yonadav Shavit; Jonathan Frankle (CoRR, 2022)

Tools for Verifying Neural Models' Training Data. - Dami Choi; Yonadav Shavit; David Kristjanson Duvenaud (NeurIPS, 2023)

What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring. - Yonadav Shavit (CoRR, 2023)

Frontier AI Regulation: Managing Emerging Risks to Public Safety. - Markus Anderljung; Joslyn Barnhart; Anton Korinek; Jade Leung; Cullen O'Keefe; Jess Whittlestone; Shahar Avin; Miles Brundage; Justin Bullock; Duncan Cass-Beggs; Ben Chang; Tantum Collins; et al. (CoRR, 2023)