137 citations · 248 across the 25 of their papers we have counts for
4 papers · 1 filter
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…
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Tobias Sutter, Andreas Krause, Daniel Kuhn
Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to train…
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…
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…