arXiv:2609.14045v1 Announce Type: new
Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fe...
By Yingxu Wang, Kunyu Zhang, Siyang Gao
arXiv:2610.00873v1 Announce Type: cross
Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between...
By Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan, Kunpeng Liu, Haifeng Chen, Yanjie Fu
arXiv:2606. 11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult.
By Ruxue Shi, Yili Wang, Mengnan Du, Hangting Ye, Yi Chang, Xin Wang
INCONVENIENT is a new benchmark that evaluates how machine‑learning models, especially large language models, generalize when trained on convenience samples that bias feature–target relationships. Unlike prior distribution‑shift tests that rely on natural domain splits, INCONVENIENT explicitly perturbs these relationships while keeping the deployment population unchanged, allowing controlled study of shift severity and direction. The benchmark covers 200 tabular classification and regression datasets and produces about 15,500 shifted training distributions, revealing significant performance degradation.
By Sara Rezaeimanesh, Chad Klochko, Kundan Thind, Mohammad Ghassemi
The paper critically evaluates common few‑shot learning protocols that rely on pre‑training a model on a large auxiliary set with classes disjoint from the target but drawn from the same visual domain. By comparing no pre‑training, class‑disjoint in‑domain pre‑training, supervised out‑of‑domain pre‑training, and label‑free out‑of‑domain pre‑training across eight datasets and three architectures, the authors find that in‑domain pre‑training yields a 33.41‑point average improvement, while out‑of‑domain pre‑training offers a 23.75‑point gain, revealing a 9.66‑point optimistic bias due to domain overlap. They also demonstrate that a label‑free augmentation strategy can match supervised out‑of‑domain performance and propose a descriptor‑based source‑selection method that closely approximates oracle selection, underscoring the need to move beyond in‑domain pre‑training as the default evaluation protocol.
By Alejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier Gallego
The paper introduces Adaptive Dual-Teacher Distillation with Subnetwork Rectification (DDSR), a method for black-box domain adaptation that fuses predictions from a black-box source model and a vision‑language model to generate reliable pseudo‑labels for the target domain. DDSR uses adaptive prediction fusion, subnetwork regularization to prevent overfitting, iterative refinement of pseudo‑labels and prompts, and class‑wise prototypes for self‑training. Experiments on several benchmark datasets show that DDSR outperforms existing state‑of‑the‑art approaches, even those that have access to source data or source model parameters.
By Zhe Zhang, Jing Li, Wanli Xue, Xu Cheng, Jianhua Zhang, Qinghua Hu, Shengyong Chen