Yonadav Shavit, Ben Edelman, and Brian Axelrod, “Causal Strategic Linear Regression” (Feb 2020). (paper). To be published in the Proceedings of ICML 2020.
Yonadav Shavit, Nadia Figueroa, Seyed Sina Mirrazavi Salehian, and Aude Billard “Learning Augmented Joint-Space Task-Oriented Dynamical Systems: A Linear Parameter Varying and Synergetic Control Approach” (2018) (paper). Accepted to the IEEE Robotics and Automation Letters, vol. 3, no. 3, pp.2718-2725, July 2018.
Yonadav Shavit and William S. Moses, “Extracting Incentives from Black-Box Decisions” (Dec 2019). (paper, blog). Appeared at the 2019 NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy.
Jianzhun Du, Andrew Ross, Yonadav Shavit, and Finale Doshi-Velez, “Controlled Direct Effect Priors for Bayesian Neural Networks” (Dec 2019). (paper). Appeared at the 2019 NeurIPS Workshop on Bayesian Deep Learning.
Yonadav Shavit and Boriana Gjura, “Exploring the use of Lipschitz Neural Networks for Automating the Design of Differentially Private Mechanisms” (Nov 2019). (paper Appeared at the 2019 Theory and Practice of Differential Privacy Workshop.
Yonadav Shavit, “Learning Environment Simulators from Sparse Signals” (Aug 2017). (paper, blog, code)