arXiv:2605. 23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data.
By Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen
arXiv:2609.09572v1 Announce Type: new
Abstract: Synthetic data has become a promising way to scale model training beyond limited human-generated data but it may also induce strong model collapse (Doh...
By Jichu li, Difan Zou
arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.
By Davide Pirovano, Federico Milanesio, Michele Caselle, Piero Fariselli, Matteo Osella
arXiv:2602. 17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments.
By Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml
The paper demonstrates that a tabular foundation model can achieve strong generalization using only a single real table for self‑supervised pre‑training, challenging the belief that large synthetic or real datasets are necessary. By systematically pre‑training and evaluating across diverse benchmarks, the authors show that the number and quality of tasks that can be derived from a dataset are critical for downstream performance. This finding suggests that carefully constructed task sets from limited data can enable effective transfer learning in tabular models.
By Junwei Ma, Nour Shaheen, Alex Labach, Amine Mhedhbi, Frank Hutter, Anthony L. Caterini, Valentin Thomas
arXiv:2607. 02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns.
By Lei Sun, Yusuke Tanaka, Tomoharu Iwata