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2020 – today
- 2024
[i5]Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter, Volkan Cevher, John Lygeros:
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces. CoRR abs/2405.15509 (2024)- 2022
- [c3]Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher:
Proximal Point Imitation Learning. NeurIPS 2022 - [i4]Angeliki Kamoutsi, Goran Banjac, John Lygeros:
Stochastic convex optimization for provably efficient apprenticeship learning. CoRR abs/2201.00039 (2022) - [i3]Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher:
Proximal Point Imitation Learning. CoRR abs/2209.10968 (2022) - 2021
- [c2]Angeliki Kamoutsi, Goran Banjac, John Lygeros:
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations. ICML 2021: 5257-5268 - [i2]Angeliki Kamoutsi, Goran Banjac, John Lygeros:
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations. CoRR abs/2112.14004 (2021)
2010 – 2019
- 2017
- [j1]Angeliki Kamoutsi
, Tobias Sutter, Peyman Mohajerin Esfahani, John Lygeros:
On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems. IEEE Control. Syst. Lett. 1(1): 134-139 (2017) - [c1]Tobias Sutter, Angeliki Kamoutsi, Peyman Mohajerin Esfahani, John Lygeros:
Data-driven approximate dynamic programming: A linear programming approach. CDC 2017: 5174-5179 - [i1]Angeliki Kamoutsi, Tobias Sutter, Peyman Mohajerin Esfahani, John Lygeros:
On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems. CoRR abs/1703.09005 (2017)
Coauthor Index
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