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: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:2307. 05213v3 Announce Type: replace-cross Abstract: Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.
By Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali \.Irfan Mahmuto\u{g}ullar{\i}, Brandon Amos, Tias Guns, Michele Lombardi
The paper introduces a decision‑focused learning framework for mean‑variance portfolio optimization that embeds the Karush‑Kuhn‑Tucker optimality conditions of the lower‑level optimization into a single‑level learning problem. This approach preserves budget and short‑sale constraints while remaining tractable for standard nonlinear solvers. Experiments on real‑world ETF data across two asset universes demonstrate superior performance on multiple investment metrics and highlight the benefits of the proposed regularization.
By Kensei Nosaka, Shunnosuke Ikeda, Yuichi Takano
arXiv:2607. 21773v1 Announce Type: new Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance in the covariate feature space.
By Aakil Caunhye, Xuefei Lu, Belen Martin-Barragan
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