Split conformal prediction provides distribution-free prediction intervals with finite-sample marginal coverage, but produces constant-width intervals that overcover in low-variance regions and undercover in high-variance regions. Existing adaptive methods require training auxiliary models. We propose Leverage-Weighted Conformal Prediction (LWCP), which weights nonconformity scores by a function of the statistical leverage -- the diagonal of the hat matrix -- deriving adaptivity from the geometry of the design matrix rather than from auxiliary model fitting. We prove that LWCP preserves finite-sample marginal validity for any weight function; achieves asymptotically optimal conditional coverage at essentially no width cost when heteroscedasticity factors through leverage; and recovers the form and width of classical prediction intervals under Gaussian assumptions while retaining distribution-free guarantees. We further establish that randomized leverage approximations preserve coverage exactly with controlled width perturbation, and that vanilla CP suffers a persistent, sample-size-independent conditional coverage gap that LWCP eliminates. The method requires no hyperparameters beyond the choice of weight function and adds negligible computational overhead to vanilla CP. Experiments on synthetic and real data confirm the theoretical predictions, demonstrating substantial reductions in conditional coverage disparity across settings.
Multi-Agent Conformal Prediction with Personalized Statistical Validity
Department of Electronic Systems（电子系统系）；Aalborg University（奥尔堡大学）；Department of Mathematical Sciences（数学科学系）；Institut de Mathématiques de Toulouse（图卢兹数学研究所）；Université de Toulouse（图卢兹大学）
Probabilistic Conformal Coverage Guarantees in Small-Data Settings
Berkeley Synchrotron Infrared Structural Biology Program（伯克利同步辐射红外结构生物学项目）；Center for Advanced Mathematics in Energy Research Applications（能源研究应用高级数学中心）；Molecular Biophysics and Integrated Bioimaging Division（分子生物物理与整合生物成像部门）
Generalized Conformal Predictive Systems Under Distributional Shifts
IDLab Seminar for Statistics（统计研究所研讨会）；Department of Electronics（电子系）；ETH Zurich（苏黎世联邦理工学院）；Information Systems Zurich, Switzerland（苏黎世信息系统，瑞士）；Ghent University（根特大学）
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
Hasso Plattner Institute for Digital Engineering（哈索·普拉特纳数字工程学院）；University of California, Irvine（加利福尼亚大学欧文分校）；Hasso Plattner Institute for Digital Health at the Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院哈索·普拉特纳数字健康研究所）
Inria（法国国家信息与自动化技术研究所）；Ecole Normale Supérieure（巴黎高等师范学院）；PSL Research University（巴黎 sciences-et-lettres 大学）；University of California, Berkeley（加州大学伯克利分校）
Conformal prediction for frequency-severity modeling
School of Applied Mathematics, Getulio Vargas Foundation（应用数学学院，格尔瓦斯基金会）；Insper Institute of Education and Research（Insper教育与研究学院）；SUSEP - Superintendence of Private Insurance（私人保险监管局）；UERJ - State University of Rio de Janeiro（里约热内卢州立大学）
ConfHit: Conformal Generative Design with Oracle Free Guarantees
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）；Department of Statistics and Data Science, Wharton School, University of Pennysylvania（宾夕法尼亚大学沃顿商学院统计与数据科学系）
