arXiv:2608.29789v1 Announce Type: cross
Abstract: Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliabl...
By Kehan Long, Yiqi Zhao, Pol Mestres, Lars Lindemann, Nikolay Atanasov, Jorge Cort\'es
arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.
By Wenbin Zhou, Shixiang Zhu
arXiv:2402. 07407v3 Announce Type: replace-cross Abstract: We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.
By Yiqi Zhao, Xinyi Yu, Matteo Sesia, Jyotirmoy V. Deshmukh, Lars Lindemann
arXiv:2511.15146v2 Announce Type: replace
Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straightforwa...
By Eugene Ndiaye
arXiv:2605.08506v3 Announce Type: replace
Abstract: Robust optimization (RO) provides a principled framework for decision-making under uncertainty, but its performance critically depends on the choic...
By Shuyi Chen, Wenbin Zhou, Shixiang Zhu
arXiv:2504. 10796v4 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) is widely used for decision-making under uncertainty, but its adversarial focus on worst-case loss can lead to overly conservative policies.
By Lukas-Benedikt Fiechtner, Jose Blanchet