7 papers
The Fragility of Learning LQG Controllers
Bruce D. Lee, Anastasios Tsiamis, Nikolai Matni +2
Learning methods are increasingly used to synthesize controllers from data, yet existing sample-complexity characterizations for continuous control are sharp only in the fully obse…
Distributionally Robust Optimization over Wasserstein Balls with i.i.d. Structure
Andrey Kharitenko, Marta Fochesato, Anastasios Tsiamis +2
We consider distributionally robust optimization problems where the uncertainty is modeled via a structured Wasserstein ambiguity set. Specifically, the ambiguity is restricted to…
Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control
Shengling Shi, Anastasios Tsiamis, Bart De Schutter
This work analyzes how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon…
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
Francesco Micheli, Efe C. Balta, Anastasios Tsiamis +1
We address the challenge of sequential data-driven decision-making under context distributional uncertainty. This problem arises in numerous real-world scenarios where the learner…
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Aren Karapetyan, Efe C. Balta, Anastasios Tsiamis +2
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the…
Semismooth Newton Methods for Risk-Averse Markov Decision Processes
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis +1
Inspired by semismooth Newton methods, we propose a general framework for designing solution methods with convergence guarantees for risk-averse Markov decision processes. Our appr…