arXiv AI July 8, 2026 By Jean-Francois Bonbhel

K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)

arXiv:2607. 05903v1 Announce Type: cross Abstract: We present K-ABENA (K-Adaptive Backpropagation with Error-based N-exclusion Algorithm), a selective gradient computation framework that reduces per-iteration training cost by excluding a fraction of low-loss ("minor") observations from the backward pass.

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safety

arXiv Statistics ML

Sep 10

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions

arXiv:2603.15576v2 Announce Type: replace-cross Abstract: This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly...

By Quoc Tran-Dinh, Nghia Nguyen-Trung

reinforcement-learning

arXiv Machine Learning

1d ago

Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning

arXiv:2610. 00436v1 Announce Type: new Abstract: Online batch selection fine-tunes a language model on the most useful part of each candidate batch.

By Hongyu Chen, Xinyi Luo, Ming Zhao, Lin Tang, Zihan Xu, Jing Li, Yuxuan Wang, Haoran Deng, Wei Zhang

llms fine-tuning

arXiv Machine Learning

Jun 9

Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning

arXiv:2602. 02431v2 Announce Type: replace-cross Abstract: It is folklore that reusing training data more than once can improve the statistical efficiency of gradient-based learning.

By Filip Kova\v{c}evi\'c, Hong Chang Ji, Denny Wu, Mahdi Soltanolkotabi, Marco Mondelli

arXiv Machine Learning

Jul 14

Demixing Sparse Signals from Nonlinear Observations using Generalized Non-convex Regularization

arXiv:2607. 10618v1 Announce Type: cross Abstract: We consider the recovery of a pair of sparse vectors from a limited number of nonlinear observations of their superposition: $y_i=g(\inner{\ba_i}{\bPhi\bw^\ast+\bPsi\bz^\ast})+e_i$, $i=1,\dots,m$, with $m\ll n$, incoherent orthonormal bases $\bPhi,\bPsi$, a scalar link $g$, and noise $e_i$ that may be heavy-tailed or contaminated.

By Raziyeh Takbiri

agents efficiency safety

arXiv Machine Learning

Jul 14

Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective

arXiv:2607. 11146v1 Announce Type: new Abstract: We study the coupled objective J_K^WOR = E_{S ~ PL-WOR_K}[max_{i in S} R_i]: the expected maximum reward of a size-K Plackett-Luce draw without replacement, the law of Gumbel-Top-K / Stochastic Beam Search decoding.

By Melveena Jolly, Midhun Xavier

arXiv Machine Learning

Aug 13

A New First-Order Meta-Learning Algorithm with Convergence Guarantees

arXiv:2409. 03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems.

By El Mahdi Chayti, Martin Jaggi

llms efficiency safety

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Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions

Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning

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