3 papers
cs.LG2024
A Generalization Result for Convergence in Learning-to-Optimize
Michael Sucker, Peter Ochs
Learning-to-optimize leverages machine learning to accelerate optimization algorithms. While empirical results show tremendous improvements compared to classical optimization algor…
cs.LG2024
A Probabilistic Framework for Learnable Optimization Algorithms
Peter Ochs, Michael Sucker
We propose a statistical-learning framework for optimization algorithms. The framework is based on probability distributions over optimization trajectories induced by a distributio…
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…