arXiv:2607. 13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks.
By R\'emy Chapelle (CESP, CB, EVDG), Nicolas Vayatis (CB), Bruno Falissard (CESP), Mohammed Sedki (CESP)
arXiv:2608. 15290v1 Announce Type: cross Abstract: The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction.
By Mandy Yao (University of Toronto), Meredith Franklin (University of Toronto)
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
arXiv:2609.06426v1 Announce Type: new
Abstract: Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjuncti...
By Shahrzad Behzadimanesh, Pierre Le Bodic, Geoffrey I. Webb, Mario Boley
arXiv:2603.05575v2 Announce Type: replace-cross
Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a blac...
By Yang Sui, Jin Zhou, Hua Zhou, Xiaowu Dai
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