arXiv Statistics ML

Fenchel-Young Duality Gaps: Certified Early Stopping for Regularized Inverse Problems

The paper introduces computable error bounds and a certified early‑stopping criterion for regularized inverse problems by exploiting an exact Fenchel–Young duality‑gap identity. The total duality gap splits into a data‑fidelity loss and a regularizer loss, both expressed as Fenchel–Young losses that are oracle‑free and vanish exactly at Mirror Alignment. Using a constructive Brønsted–Rockafellar approach, the authors build a dual‑feasible proxy via a proximal step in the fidelity geometry, enabling an early‑stopping rule based on the regularizer loss.

arXiv Machine Learning
Aug 11

Constrained Learning with Universally Learnable Concept Classes

arXiv:2608. 08414v1 Announce Type: new Abstract: We study constrained statistical learning over infinite-dimensional hypothesis classes in the fully nonconvex setting, and establish universal PACC learnability of the solutions of dual algorithms: Probably Approximately Correct on Constraints, guaranteeing optimality and constraint satisfaction at once.

By Herlock SeyedAbolfazl Rahimi, Spyridon Pougkakiotis, Dionysis Kalogerias
arXiv Machine Learning
Jul 21

Scaling Limits of Constant-Stepsize SGD at Flat Minima

arXiv:2607. 16384v1 Announce Type: new Abstract: For stochastic gradient descent (SGD) with a constant stepsize $\alpha$, the invariant law of the iterates, centered at a minimizer, describes the behavior of the algorithm over long time horizons.

By Jingyi Zhang, Cheng Mao, Debankur Mukherjee