arXiv:2606. 25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions.
By Tinghan Ye, Shuaicheng Tong, Changkun Guan, Beste Basciftci, Pascal Van Hentenryck
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
By Beichen Wan, Mo Liu
arXiv:2505. 04757v2 Announce Type: replace Abstract: This paper introduces a novel approach to contextual stochastic optimization, integrating operations research and machine learning to address decision-making under uncertainty.
By Louis Bouvier, Thibault Prunet, Vincent Lecl\`ere, Axel Parmentier
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
By St\'ephane Eilles-Chan Way, Hugo Percot, Quentin Cappart, Tias Guns, Louis-Martin Rousseau
arXiv:2608. 15124v1 Announce Type: new Abstract: In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features.
By Gar Goei Loke, Qinshen Tang, Yangge Xiao, Xun Zhang
arXiv:2609.01493v1 Announce Type: cross
Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-w...
By Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue