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: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:2608. 03111v1 Announce Type: new Abstract: Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width.
By Ryuichi Kanoh
Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous single-axis capacity parameter has not been established.