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cs.LG2026
Optimal Transport PAC-Bayes Bounds
Armin Beck, Peter Ochs, Jalal Fadili
PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in terms of the Kullback-Leibler (K…
cs.LG2024
From Learning to Optimize to Learning Optimization Algorithms
Camille Castera, Peter Ochs
Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not…
cs.LG2024
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
Michael Sucker, Jalal Fadili, Peter Ochs
We use the PAC-Bayesian theory for the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable…