arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.
By Yan-Feng Xie, Shuche Wang, Peng Zhao, Zhi-Hua Zhou
arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.
By Sebastian Reboul, H\'el\`ene Halconruy
arXiv:2604. 19592v2 Announce Type: replace Abstract: We give a Gordon-Greenwald-Marks (GGM) style black-box reduction from online learning to online multicalibration.
By Gabriele Farina, Juan Carlos Perdomo
arXiv:2605. 14953v2 Announce Type: replace Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identified with a pre-specified target probability $\phi$.
By Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala, Ali Sinop
arXiv:2608. 07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications.
By Joar Skalse, Edoardo Pona, Osvaldo Simeone, Nicola Paoletti
arXiv:2510. 15824v2 Announce Type: replace-cross Abstract: This article considers an online version of conformal inference, called adaptive conformal inference [ACI] and introduced by Gibbs and Cand\`es (2021): prediction sets are issued sequentially, after observing features and before the outcomes are revealed.
By Guillaume Principato, Gilles Stoltz