53 citations · 202 across the 26 of their papers we have counts for
4 papers · 1 filter
Distributionally Robust Bayesian Optimization
Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka +1
Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, whi…
Adversarially Robust Optimization with Gaussian Processes
Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka +1
In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we requi…
Towards Optimal Transport with Global Invariances
David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
Many problems in machine learning involve calculating correspondences between sets of objects, such as point clouds or images. Discrete optimal transport provides a natural and suc…
Structured Optimal Transport
David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning. Yet, its ability to capt…