arXiv Machine Learning July 8, 2026 By Xiangwu Wang, Chengwei Cao, Yicheng Song, Ran Bi, Peilin Yu

Stability Annealing Selects the Implicit Bias of Smoothed Sign Descent: A Rate-Indexed Barrier Path on Separable Data

Read the original on arXiv Machine Learning →

arXiv:2607. 06013v1 Announce Type: new Abstract: Adaptive gradient methods can favor max-margin separators that differ from gradient descent, yet a fixed positive numerical stability constant eventually changes the update geometry again.

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arXiv Machine Learning

Jul 28

Learning from the Descent Direction: Adaptive Gradient Descent under One-Sided H\"older Regularity

arXiv:2607. 22906v1 Announce Type: new Abstract: We study adaptive gradient descent for continuously differentiable, possibly nonconvex objectives under one-sided H\"older regularity.

By Arzu Ahmadova, Ismail Huseynov

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arXiv AI

Sep 2

Operational Regimes in Non-Convex Optimization: A Multiplier-Based Taxonomy

arXiv:2609.00471v1 Announce Type: cross Abstract: This paper introduces a structural taxonomy for constrained non-convex optimization based on the signature of Lagrange multipliers at KKT stationary...

By Seyed Mohsen Kazemi, Ali Movaghar, Shaahin hessabi

arXiv AI

Sep 10

The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization

arXiv:2609.06064v1 Announce Type: cross Abstract: Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical stu...

By Tianxi Zhu, Yi Xu, Xiangyang Ji

arXiv Machine Learning

Jul 13

Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization

arXiv:2405. 00914v4 Announce Type: replace-cross Abstract: We present in this paper novel accelerated fully first-order methods in \emph{Bilevel Optimization} (BLO).

By Chris Junchi Li

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arXiv Machine Learning

Jun 25

A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization

arXiv:2602. 02877v2 Announce Type: replace Abstract: This paper studies optimization for a family of problems termed $\textbf{compositional entropic risk minimization}$, in which each data's loss is formulated as a Log-Expectation-Exponential (Log-E-Exp) function.

By Xiyuan Wei, Linli Zhou, Bokun Wang, Chih-Jen Lin, Tianbao Yang

arXiv Machine Learning

Aug 6

Non-asymptotic implicit bias of logistic regression at early-stage gradient descent dynamics

arXiv:2608. 04382v1 Announce Type: new Abstract: Gradient descent has been of particular interest in modern machine learning beyond sole focus on optimization.

By Han Bao

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