220 citations · 724 across the 45 of their papers we have counts for
7 papers · 2 filters
Unifying Distributionally Robust Optimization via Optimal Transport Theory
Jose Blanchet, Daniel Kuhn, Jiajin Li +1
In recent years, two prominent paradigms have shaped distributionally robust optimization (DRO), modeling distributional ambiguity through -divergences and Wasserstein distances…
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Yves Rychener, Daniel Kuhn, Tobias Sutter
We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation…
Frequency Regulation with Storage: On Losses and Profits
Dirk Lauinger, François Vuille, Daniel Kuhn
Low-carbon societies will need to store vast amounts of electricity to balance intermittent generation from wind and solar energy, for example, through frequency regulation. Here,…
Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets
Mengmeng Li, Daniel Kuhn, Tobias Sutter
We propose policy gradient algorithms for robust infinite-horizon Markov decision processes (MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in th…
Distributionally Robust Linear Quadratic Control
Bahar Taşkesen, Dan A. Iancu, Çağıl Koçyiğit +1
Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It i…
PIQP: A Proximal Interior-Point Quadratic Programming Solver
Roland Schwan, Yuning Jiang, Daniel Kuhn +1
This paper presents PIQP, a high-performance toolkit for solving generic sparse quadratic programs (QP). Combining an infeasible Interior Point Method (IPM) with the Proximal Metho…
Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
Soroosh Shafiee, Liviu Aolaritei, Florian Dörfler +1
We study optimal transport-based distributionally robust optimization problems where a fictitious adversary, often envisioned as nature, can choose the distribution of the uncertai…