We propose conformal hyperrectangular prediction regions for multi-target regression. We propose split conformal prediction algorithms for both point and quantile regression to form hyperrectangular prediction regions, which allow for easy marginal interpretation and do not require covariance estimation. In practice, it is preferable that a prediction region is balanced, that is, having identical marginal prediction coverage, since prediction accuracy is generally equally important across components of the response vector. The proposed algorithms possess two desirable properties, namely, tight asymptotic overall nominal coverage as well as asymptotic balance, that is, identical asymptotic marginal coverage, under mild conditions. We then compare our methods to some existing methods on both simulated and real data sets. Our simulation results and real data analysis show that our methods outperform existing methods while achieving the desired nominal coverage and good balance between dimensions.
HEUDIASYC - UMR CNRS 7253（法国国家科学研究中心7253联合研究单位 启发式与诊断复杂系统实验室）；Université de Technologie de Compiègne（贡比涅技术大学）
There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions applied to deep neural networks for inductive conformal prediction. We show that the proposed method ensures efficiency and validity for multi-target regression problems on various data sets.
In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the distribution of the underlying data, except that the training and test data are assumed to be exchangeable. However, these methods bear a heavy computational cost-and, to be carried out exactly, the regression algorithm would need to be fitted infinitely many times. In practice, the conformal prediction method is run by simply considering only a finite grid of finely spaced values for the response variable. This paper de…
Stable Localized Conformal Prediction via Transduction
School of Statistics；Data Science, Nankai University, Tianjin, China；School of Mathematics \& Statistics, The University of Melbourne, Melbourne, Australia
Recently Bosc et al. (J Cheminform 11(1): 4, 2019), published an article describing a case study that directly compares conformal predictions with traditional QSAR methods for large-scale predictions of target-ligand binding. We consider this study to be very important. Unfortunately, we have found several issues in the authors' approach as well as in the presentation of their findings.
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Columbia University（哥伦比亚大学）
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples from an estimated generative model. It is efficient and compatible with either explicit or implicit conditional generative models. Theoretically, we show that PCP guarantees correct marginal coverage with finite samples. Empirically, we study PCP on a variety of simulated and real datasets. Compared to existing methods for conformal inference, PCP provides sharper…
Academy of Statistics and Interdisciplinary Sciences, KLATASDS-MOE（统计与交叉科学研究院（KLATASDS-MOE））；East China Normal University（华东师范大学）；London School of Economics and Political Science（伦敦政治经济学院）；Bytedance（字节跳动）
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. In this paper, we develop a novel procedure to produce reliable interval estimators for a target policy's return starting from any initial state. Our proposal accounts for the variability of the return around its expectation, focuses on the individual effect and offers valid uncertainty quantification. Our main idea lies in desi…
Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding
Department of Biostatistics, Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院生物统计系）；Department of Biostatistics and Informatics, Colorado School of Public Health（科罗拉多公共卫生学院生物统计与信息学系）；Department of Data Science, Dana Farber Cancer Institute（丹娜-法伯癌症研究所数据科学系）
Audited Conformal Prediction for Classification under Unknown Distribution Shift
Department of Data Sciences and Operations, University of Southern California（数据科学与运营系，南加州大学）；AI Foundations, Capital One（Capital One人工智能基础）；Department of Data Sciences and Operations, Thomas Lord Department of Computer Science, University of Southern California（数据科学与运营系，托马斯·劳德计算机科学系，南加州大学）
Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
Department of Biostatistics, Vanderbilt University Medical Center（生物统计学系，范德比尔特大学医学中心）；Department of Informatics, King’s College London（信息学系，伦敦国王学院）；Department of Biostatistics, MD Anderson Cancer Center（生物统计学系，MD安德森癌症中心）
Normalizing flows are generative models that provide tractable density estimation via an invertible transformation from a simple base distribution to a complex target distribution. However, this technique cannot directly model data supported on an unknown low-dimensional manifold, a common occurrence in real-world domains such as image data. Recent attempts to remedy this limitation have introduced geometric complications that defeat a central benefit of normalizing flows: exact density estimation. We recover this benefit with Conformal Embedding Flows, a framework for designing flows that lea…
Georgia Institute of Technology（佐治亚理工学院）；Nagoya University（名古屋大学）；RIKEN AIP（理化学研究所先进智能项目中心）
Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses and features. It has a coverage guarantee at any nominal level without additional assumptions on their distribution. Its computation deplorably requires a refitting procedure for all replacement candidates of the target response. In regression settings, this corresponds to an infinite number of model fits. Apart from relatively simple estimators that can be written as pieces of linear function of the response, effic…
A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It
School of Science and Technology, Champions College Prep（科学与技术学院，冠军大学预科）
