The paper explores when different decision proxies are appropriate for decision‑focused learning (DFL) in optimization problems with uncertainty. It identifies problem properties that justify using a particular proxy and proposes alternative proxies that maintain learning complexity. Experiments on continuous, discrete, and objective‑ or constraint‑uncertain problems demonstrate the effectiveness of these approaches.
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.
arXiv:2312.16730v2 Announce Type: replace-cross Abstract: Interactive decision making is the problem of learning to act well in an unknown environment, using the data that one's own actions generate...
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.
arXiv:2607. 14817v1 Announce Type: cross Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.
arXiv:2601. 22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials.
arXiv:2601.22736v3 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having...
arXiv:2607. 14407v1 Announce Type: cross Abstract: Many signal processing systems ultimately exist to {act}.
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
The paper extends ex‑ante evaluation of Predict‑Then‑Optimize methods from binary to multiclass classification by simulating predictions at specified performance levels and mapping prediction errors to decision regret. It introduces a first‑order approximation that estimates regret from individual misclassifications, reducing computational effort. Experiments show the simulation accurately reproduces target performance and that the approximation is close for some problems, though it falters when simultaneous misclassifications interact significantly.
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.
arXiv:2504. 18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with limited formal justification and evaluations.
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.