Tobias Sutter
Now showing 1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Computing Optimal Joint Chance Constrained Control Policies(2025-02-26); John LygerosWe consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard dynamic programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably optimal and easy-to-compute policies are still missing. We fill this gap by augmenting the dynamics via a binary state, allowing us to characterize the optimal policies and develop a dynamic programming-based solution method.Type:journal articleJournal:IEEE Transactions on Automatic ControlVolume:70Issue:7Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributional Adversarial Attacks and Training in Deep Hedging(2025-11); Type:conference paperJournal:The Thirty-ninth Annual Conference on Neural Information Processing Systems (Neurips 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimal Learning via Moderate Deviations Theory(2025-09-02)This paper proposes a statistically optimal approach for learning a function value using a confidence interval in a wide range of models, including general non-parametric estimation of an expected loss described as a stochastic programming problem or various SDE models. More precisely, we develop a systematic construction of highly accurate confidence intervals by using a moderate deviation principle-based approach. It is shown that the proposed confidence intervals are statistically optimal in the sense that they satisfy criteria regarding exponential accuracy, minimality, consistency, mischaracterization probability, and eventual uniformly most accurate (UMA) property. The confidence intervals suggested by this approach are expressed as solutions to robust optimization problems, where the uncertainty is expressed via the underlying moderate deviation rate function induced by the data-generating process. We demonstrate that for many models these optimization problems admit tractable reformulations as finite convex programs even when they are infinite-dimensional.Type:conference lecture