Publications by Daniel Kuhn | EPFL Graph Search

Global Group Fairness in Federated Learning via Function Tracking

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probab ...

2025

Distributionally Robust Optimization

Daniel Kuhn, Wolfram Wiesemann

Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a fami ...

Oxford University Press (OUP)2025

Distributionally Robust Optimal Allocation with Costly Verification

Daniel Kuhn

We consider the mechanism design problem of a principal allocating a single good to one of several agents without monetary transfers. Each agent desires the good and uses it to create value for the principal. We designate this value as the agent's private ...

2025

Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Zhi Chen, 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 constraints with right-hand side uncertainty, our reformulation amounts to a mixed ...

2024

Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints

Daniel Kuhn, François Richard Vuille, Dirk Lauinger

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 robust optimization problem that maximizes a vehicle owner’s expected profi ...

2024

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

Daniel Kuhn, Napat Rujeerapaiboon, Kilian Schindler, Wolfram Wiesemann

Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout and the steady growth in photovoltaic capacity. The reduced profitability of peak/off- peak arbitrage forces hydropower producers to reco ...

2024

A Large Deviations Perspective on Policy Gradient Algorithms

Daniel Kuhn, Mengmeng Li, Wouter Jongeneel

Motivated by policy gradient methods in the context of reinforcement learning, we derive the first large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-Łojasiewicz co ...

2024

Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions

Daniel Kuhn, Napat Rujeerapaiboon, Cagil Kocyigit

We study a robust auction design problem with a minimax regret objective, where a seller seeks a mechanism for selling multiple items to multiple anonymous bidders with additive values. The seller knows that the bidders' values range over a box uncertainty ...

2024

Small Errors in Random Zeroth-Order Optimization are Imaginary

Daniel Kuhn, Wouter Jongeneel

Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some noisy gradient estimator that can be computed from a small number of function evaluations. This estimator is constructed ra ...

Society for Industrial & Applied Mathematics (SIAM)2024

Multi-Period Optimal Power Flow: Convex Relaxations and Parallel Algorithms

Daniel Kuhn, Tianshu Yang

Ensuring reliable operation in power grids given the complexity of modern electricity networks with increasing demands, varying generation and flows present significant computational challenges. Addressing these challenges, we develop efficient methods for ...

Institute of Electrical and Electronics Engineers Inc.2024