The paper proposes a two‑stage framework that first fine‑tunes a large language model (LLM) and then rectifies its outputs, allocating limited labeled data optimally between the stages. It argues that the usual mean‑squared‑error objective for fine‑tuning misaligns with the downstream rectification, and instead suggests minimizing prediction‑error variance for mean estimation or a scalarized variance metric for general M‑estimation. Empirical results confirm that this variance‑based fine‑tuning, combined with optimal data allocation, yields significant efficiency gains over using either fine‑tuning or rectification alone, or using the conventional objective.
By Zikun Ye, Jinglong Zhao, Lei Wang
The paper introduces DUO, a framework for Deep Imbalanced Regression that models each prediction as a conditional Gaussian to capture instance‑level uncertainty. By decoupling mean and variance optimization, DUO enhances learning signals for tail samples and mitigates gradient coupling that hampers hard examples. A distribution‑guided contrastive learning component further refines feature representations, leading to state‑of‑the‑art performance on several visual and biological regression benchmarks.
By Juncheng Zhou, Jiaxi Lu, Weijing Zeng, Zhong Li, Hao Qi, Jingsong Cui
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
By Kun Jin, James Harrison, Jiawei Li, Sihan Liu, Jiayi Liu, Randolph Linderman, Yuening Li, Arnab Bhadury, Sourabh Prakash Bansod, Liang Liu, Jasper Snoek
The paper investigates how synthetic pretraining priors used in tabular foundation models (TFMs) influence downstream performance. By reconstructing the synthetic data generators of four TFMs and comparing their generated tasks to two popular tabular benchmarks using structural descriptors, the authors measure structural coverage and normalized density. They find that some generators provide broader and denser support for benchmark tasks, and that stronger synthetic-to-benchmark support generally correlates with better model performance.
By He Zhao, Ryan Thompson, Daniel M. Steinberg, Ashfaqur Rahman, Edwin V. Bonilla, Cheng Soon Ong
The paper introduces FOCUS, a cross‑dataset benchmark for retinal fundus models that evaluates vision‑only encoders, vision‑language dual encoders, and multimodal large language models across ten public datasets. It measures ranking performance, calibration, subgroup disparities, and image‑quality robustness for tasks such as diabetic retinopathy and glaucomatous optic neuropathy. Results show no single model family dominates, with vision‑only encoders ranking highest on average, while fine‑tuning improves in‑domain performance but varies in external transfer.
By David Restrepo, Chenwei Wu, Luis Filipe Nakayama, Miguel L. Martins, Stergios Christodoulidis, Maria Vakalopoulou, Enzo Ferrante
arXiv:2509. 20345v3 Announce Type: replace-cross Abstract: The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference.
By Meshi Bashari, Yonghoon Lee, Roy Maor Lotan, Edgar Dobriban, Yaniv Romano
arXiv:2607. 16053v1 Announce Type: cross Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints.
By Claudia Skok Gibbs
arXiv:2610.16990v1 Announce Type: cross
Abstract: Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. T...
By Yu-Sheng Li, Jun-Peng Jiang, Han-Jia Ye
arXiv:2607. 07083v1 Announce Type: cross Abstract: Subsampling significantly reduces the number of measurements, thereby streamlining data processing and transfer overhead, and shortening acquisition time across diverse real-world applications.
By Beomgu Kang, Hyunseok Seo
arXiv:2605.07938v2 Announce Type: replace
Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...
By Sachini Weerasekara, Natasha Darras, Sagar Kamarthi, Colles Price, Jacqueline Isaacs
arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.
By German Gritsai, Megan Richards, Maxime M\'eloux, Kyunghyun Cho, Maxime Peyrard
arXiv:2610.32492v1 Announce Type: cross
Abstract: Large language models serve applications with diverse inference budgets, but training and maintaining separate models for each budget incurs substant...
By Yudong Zhang, Wei Ding, Kaili Zheng, Yuchen Ding, Zhenyu Zhang