We introduce our method, conformal highest conditional density sets (CHCDS), that forms conformal prediction sets using existing estimated conditional highest density predictive regions. We prove the validity of the method, and that conformal adjustment is negligible under some regularity conditions. In particular, if we correctly specify the underlying conditional density estimator, the conformal adjustment will be negligible. The conformal adjustment, however, always provides guaranteed nominal unconditional coverage, even when the underlying model is incorrectly specified. We compare the proposed method via simulation and a real data analysis to other existing methods. Our numerical results show that CHCDS is better than existing methods in scenarios where the error term is multi-modal, and just as good as existing methods when the error terms are unimodal.
A flexible conformal inference method is developed to construct confidence intervals for the frequencies of queried objects in very large data sets, based on a much smaller sketch of those data. The approach is data-adaptive and requires no knowledge of the data distribution or of the details of the sketching algorithm; instead, it constructs provably valid frequentist confidence intervals under the sole assumption of data exchangeability. Although our solution is broadly applicable, this paper focuses on applications involving the count-min sketch algorithm and a non-linear variation thereof.…
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Department of Economics and Business, Pompeu Fabra University（巴塞罗那庞培法布拉大学经济与商业系）；ICREA；Barcelona Graduate School of Economics（巴塞罗那经济研究生院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Department of Biostatistics, Harvard University（哈佛大学生物统计学系）
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）
School of Statistics and Data Science, LPMC, KLMDASR and LEBPS, Nankai University（南开大学统计与数据科学学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems
Technical University of Denmark（丹麦技术大学）；Heriot-Watt University（赫瑞瓦特大学）；Maxwell Institute for Mathematical Sciences（麦克斯韦数学研究所）；Université de Paris Cité（巴黎西岱大学）
School of Mathematics and Statistics（数学与统计学学院）；Beijing Jiaotong University（北京交通大学）；Department of Mathematical and Statistical Sciences（数学与统计学系）；University of Alberta（阿尔伯塔大学）
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
Department of Statistics, Stanford University（斯坦福大学统计学系）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计学与数据科学系）；Department of Mathematics, Stanford University（斯坦福大学数学系）
A Systematic Review of Conformal Inference Procedures for Treatment Effect Estimation: Methods and Challenges
Engineering Mathematics and Computing Lab Heidelberg University（海德堡大学工程数学与计算实验室）；Heidelberg University（海德堡大学）；Heidelberg Institute for Theoretical Studies, Data Mining and Uncertainty Quantification Group（海德堡理论研究所数据挖掘与不确定性量化组）；Medical Faculty Mannheim of Heidelberg University, Data Analysis and Modeling in Medicine（海德堡大学曼海姆医学系医学数据分析与建模）
Error-quantified Conformal Inference for Time Series
School of Statistics and Data Science, LPMC, KLMDASR and LEBPS, Nankai University（统计与数据科学学院，LPMC，KLMDASR和LEBPS，南开大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）
Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；IT University of Copenhagen（哥本哈根信息技术大学）；Pioneer Centre for Artificial Intelligence（先锋人工智能中心）；Unbabel（Unbabel公司）
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals
Département de Mathématiques, Université du Québec à Montréal（魁北克大学蒙特利尔分校数学系）；Institut Intelligence et Données, Université Laval（拉瓦尔大学智能与数据研究所）；CNRS - Université de Montréal（法国国家科学研究中心-蒙特利尔大学）；CRM – CNRS Aix Marseille Univ, CNRS, AMSE, Marseille, France（法国国家科学研究中心-阿维尼昂大学）
Minimum Volume Optimal Transport for Multivariate Conformal Prediction
School of Statistics and Data Science, Nankai University（南开大学统计与数据科学学院）；School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学科学学院）；School of Management, Fudan University（复旦大学管理学院）
Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditional coverage, that is, coverage for every new testing point. However, without strong assumptions, conditional coverage is unachievable. Given this limitation, the literature has focused on methods with asymptotical conditional coverage. In order to obtain this property, these methods require strong conditions on the dependence between the target variable and the features. We introduce two conformal methods based on …
