220 citations · 724 across the 45 of their papers we have counts for
33 papers · 1 filter
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…
On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given…
Discrete Optimal Transport with Independent Marginals is #P-Hard
Bahar Taşkesen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn +1
We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vector…
Distributionally Robust Optimization with Markovian Data
Mengmeng Li, Tobias Sutter, Daniel Kuhn
We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples ge…
A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle
Jianzhe Zhen, Daniel Kuhn, Wolfram Wiesemann
Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertain…
Topological Linear System Identification via Moderate Deviations Theory
Wouter Jongeneel, Tobias Sutter, Daniel Kuhn
Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space. The ind…
Small errors in random zeroth-order optimization are imaginary
Wouter Jongeneel, Man-Chung Yue, Daniel Kuhn
Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some gradient estimator that can be computed from a…
A Planner-Trader Decomposition for Multi-Market Hydro Scheduling
Kilian Schindler, Napat Rujeerapaiboon, Daniel Kuhn +1
Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. Th…
Efficient Learning of a Linear Dynamical System with Stability Guarantees
Wouter Jongeneel, Tobias Sutter, Daniel Kuhn
We propose a principled method for projecting an arbitrary square matrix to the non-convex set of asymptotically stable matrices. Leveraging ideas from large deviations theory, we…
A Pareto Dominance Principle for Data-Driven Optimization
Tobias Sutter, Bart P. G. Van Parys, Daniel Kuhn
We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that…
Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints
Dirk Lauinger, François Vuille, Daniel Kuhn
Problem definition: Vehicle-to-grid increases the low utilization rate of privately owned electric vehicles by making their batteries available to electricity grids. We formulate a…
On Linear Optimization over Wasserstein Balls
Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distribut…
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn +1
We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The propo…
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these…
Wasserstein Distributionally Robust Kalman Filtering
Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn +1
We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal es…
Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constr…
Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a -dimensional Gaussian ran…
Distributionally robust optimization with polynomial densities: theory, models and algorithms
Etienne de Klerk, Daniel Kuhn, Krzysztof Postek
In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the…