The paper introduces Transfer Learning for Evolving Domains (TrED), a framework that models how data availability changes over time in real-world applications. TrED treats the entire trajectory of model updates as a single learning problem, rather than isolated snapshots, and defines a data availability process, a flexible learning protocol, and an evaluation criterion that scores the whole trajectory. The authors review existing transfer learning methods, noting that most are tailored to specific regimes and do not optimize the full trajectory, and argue that TrED is a well‑posed, unsolved research direction.
By Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira, Pedro Ribeiro, Pedro Saleiro, Pedro Bizarro, Carlos Soares
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
arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.
By B\'er\'enice-Alexia Jocteur (ICJ, PSPM), V\'eronique Maume-Deschamps (ICJ, PSPM), Pierre Ribereau (PSPM, ICJ)
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.
By Joonho Kim, Seyoung Park
arXiv:2606. 00558v1 Announce Type: new Abstract: Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain.
By Yuan Yao, Jin Song, Huixia Li, Tongtong Yuan, Jiaqi Wu, Yu Zhang
The paper introduces CATTLE, a transfer learning framework for disjoint tabular datasets that eliminates the need for shared features by leveraging generalized context learned through transformer projection weights. By using key, value, and query weights from source and target domains, CATTLE performs cross‑domain attention transfer in a data‑agnostic manner. Experiments on ten source‑target pairs demonstrate that CATTLE outperforms nine state‑of‑the‑art baselines, achieving the best average rank (2.9) and a 3.7% AUROC improvement.
By Kazi F. Akhter, Ibna Kowsar, Manar D. Samad
arXiv:2606. 18567v1 Announce Type: cross Abstract: This paper presents a methodology-centered transfer learning framework for fragility adaptation under domain shift, class imbalance, and scarce target labels while preserving engineering interpretability and supporting decision-making under uncertainty.
By Narges Saeednejad, Jamie Ellen Padgett
arXiv:2608. 12403v1 Announce Type: cross Abstract: Pre-trained black-box predictive functions encode knowledge distilled from massive datasets and extensive computation.
By Oh-Ran Kwon, Daeyoung Ham
arXiv:2609. 11918v1 Announce Type: new Abstract: Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples.
By Hongbo Chen, Li Charlie Xia
arXiv:2608. 03249v1 Announce Type: new Abstract: Cold-Start Active Learning (CSAL) aims to select a valuable subset from an unlabeled pool without any prior knowledge or human assistance.
By Ning Zhu, Xiaochuan Ma, Juntao Xu, Jingze Liang, Mengfei Zhao, An Chen, Liang-Jian Deng
arXiv:2509. 23689v2 Announce Type: replace Abstract: Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into one model that retains performance across different tasks.
By Mauro Conti, Ankit Gangwal, Aaryan Ajay Sharma
arXiv:2608. 09091v1 Announce Type: cross Abstract: Transfer learning is particularly useful in settings with limited training data, and within image classification it is common to transfer learn upon massive datasets like ImageNet , CIFAR-100, or COCO .
By Jing Ning, James D. Braza