Data-driven approximate dynamic programming: A linear programming approach. - Tobias Sutter; Angeliki Kamoutsi; Peyman Mohajerin Esfahani; John Lygeros (CDC, 2017)

On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems. - Angeliki Kamoutsi; Tobias Sutter; Peyman Mohajerin Esfahani; John Lygeros (IEEE Control. Syst. Lett., 2017)

Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations. - Angeliki Kamoutsi; Goran Banjac; John Lygeros (ICML, 2021)

Proximal Point Imitation Learning. - Luca Viano; Angeliki Kamoutsi; Gergely Neu; Igor Krawczuk; Volkan Cevher (NeurIPS, 2022)

Stochastic convex optimization for provably efficient apprenticeship learning. - Angeliki Kamoutsi; Goran Banjac; John Lygeros (CoRR, 2022)

Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces. - Angeliki Kamoutsi; Peter Schmitt-Förster; Tobias Sutter; Volkan Cevher; John Lygeros (NeurIPS, 2024)