arXiv:2609.14531v1 Announce Type: cross
Abstract: Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individu...
By Rohan Hore, Anirban Chatterjee, Sayantan Choudhury
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:2606. 28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others.
By Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber
The paper introduces a distributionally robust learning framework that learns the robustness mechanism’s parameters from held‑out data via bilevel optimization, avoiding extensive manual tuning. Two variants of the framework are presented: one for settings with group labels and one for settings without them. The authors provide theoretical sample‑complexity guarantees comparable to exhaustive grid search and demonstrate empirically that the method scales and performs well when both intra‑group and inter‑group distribution shifts occur simultaneously.
arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.
By Junxian Liu, Hao Zeng, Hongxin Wei
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