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cs.LG2026
Euclidean Distance-Based Flow Matching
Paul-Henrik Heilmann, Peter Ochs
Flow Matching has recently become popular as a technique for generative modeling with applications in many domains. In the standard Flow Matching formulation, we interpret the vect…
cs.LG2022
PAC-Bayesian Learning of Optimization Algorithms
Michael Sucker, Peter Ochs
We apply the PAC-Bayes theory to the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable ge…