arXiv Machine Learning July 7, 2026 By Joonho Kim, Seyoung Park

Transfer Learning in High-dimensional Ising Models

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arXiv:2607. 03005v1 Announce Type: new Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging.

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arXiv Machine Learning

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Heterogeneous transfer learning for high-dimensional regression with feature mismatch

arXiv:2412. 18081v3 Announce Type: replace-cross Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets.

By Jae Ho Chang, Massimiliano Russo, Subhadeep Paul

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arXiv Machine Learning

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Transfer Learning for Matrix Completion

arXiv:2507.02248v2 Announce Type: replace-cross Abstract: In this paper, we explore the knowledge transfer under the setting of matrix completion, which aims to enhance the estimation of a low-rank t...

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Formal Bayesian Transfer Learning via the Total Risk Prior

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arXiv Machine Learning

Aug 21

Transfer Learning in Nonparametric Regression with Deep ReLU Networks

arXiv:2608. 20255v1 Announce Type: cross Abstract: This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups.

By Junpeng Ren, Carlos Misael Madrid Padilla, Yanzhen Chen, Oscar Hernan Madrid Padilla

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arXiv AI

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Limits of Transfer Learning

The paper investigates the theoretical limits of transfer learning, demonstrating that careful selection of transferable information and its dependence on target problems is crucial. It establishes that the degree of probabilistic change in a transfer-learning algorithm imposes an upper bound on achievable improvement. These findings extend the algorithmic search framework to a broad class of learning tasks involving transfer.

By Jake Williams, Abel Tadesse, Tyler Sam, Huey Sun, George D. Montanez

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arXiv Machine Learning

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BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training

arXiv:2606. 18650v1 Announce Type: new Abstract: As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive learning trajectories.

By Jiaxing Wang, Deping Xiang, Jin Xu, Zirui Liu, Zicheng Zhang, Guoqiang Gong, Jun Fang, Chao Liu, Pengzhang Liu, Tongxuan Liu, Ke Zhang, Qixia Jiang

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