CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control

• URL: http://arxiv.org/abs/2403.07728v2
• Date: Thu, 28 Mar 2024 14:20:13 GMT
• Title: CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
• Authors: Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou,
• Abstract summary: It is important to control the real-time false coverage-statement rate (FCR) which measures the overall miscoverage level. We develop a general framework named CAP that performs an adaptive pick rule on historical data to construct a calibration set. We prove that CAP can achieve an exact selection-conditional coverage guarantee in the finite-sample and distribution-free regimes.
• Score: 4.137346786534721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of post-selection predictive inference in an online fashion. To avoid devoting resources to unimportant units, a preliminary selection of the current individual before reporting its prediction interval is common and meaningful in online predictive tasks. Since the online selection causes a temporal multiplicity in the selected prediction intervals, it is important to control the real-time false coverage-statement rate (FCR) which measures the overall miscoverage level. We develop a general framework named CAP (Calibration after Adaptive Pick) that performs an adaptive pick rule on historical data to construct a calibration set if the current individual is selected and then outputs a conformal prediction interval for the unobserved label. We provide tractable procedures for constructing the calibration set for popular online selection rules. We proved that CAP can achieve an exact selection-conditional coverage guarantee in the finite-sample and distribution-free regimes. To account for the distribution shift in online data, we also embed CAP into some recent dynamic conformal prediction algorithms and show that the proposed method can deliver long-run FCR control. Numerical results on both synthetic and real data corroborate that CAP can effectively control FCR around the target level and yield more narrowed prediction intervals over existing baselines across various settings.

Related papers

• Online scalable Gaussian processes with conformal prediction for guaranteed coverage [32.21093722162573]
  The consistency of the resulting uncertainty values hinges on the premise that the learning function conforms to the properties specified by the GP model. We propose to wed the GP with the prevailing conformal prediction (CP), a distribution-free post-processing framework that produces it prediction sets with a provably valid coverage.
  arXiv  Detail & Related papers  (2024-10-07T19:22:15Z)
• Selecting informative conformal prediction sets with false coverage rate control [0.873811641236639]
  Conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider both the classification and regression settings where the analyst may consider as informative only the sample with prediction sets small enough. We develop a unified framework for building such informative conformal prediction sets while controlling the false coverage rate (FCR) on the selected sample.
  arXiv  Detail & Related papers  (2024-03-18T22:35:43Z)
• Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage [6.010965256037659]
  Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn new test point with a prescribed probability. In such cases, marginally valid conformal prediction intervals may not provide valid coverage for the focal unit(s) due to selection bias. This paper presents a general framework for constructing a prediction set with finite-sample exact coverage conditional on the unit being selected.
  arXiv  Detail & Related papers  (2024-03-06T17:18:24Z)
• PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction [27.434939269672288]
  We use PAC-Bayes theory to obtain generalization bounds on the coverage and the efficiency of set-valued predictors. We leverage these theoretical results to provide a practical algorithm for using calibration data to fine-tune the parameters of a model and score function. We evaluate the approach on regression and classification tasks, and outperform baselines calibrated using a Hoeffding bound-based PAC guarantee on ICP.
  arXiv  Detail & Related papers  (2023-12-07T19:40:44Z)
• When Does Confidence-Based Cascade Deferral Suffice? [69.28314307469381]
  Cascades are a classical strategy to enable inference cost to vary adaptively across samples. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. Despite being oblivious to the structure of the cascade, confidence-based deferral often works remarkably well in practice.
  arXiv  Detail & Related papers  (2023-07-06T04:13:57Z)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• Improved Online Conformal Prediction via Strongly Adaptive Online Learning [86.4346936885507]
  We develop new online conformal prediction methods that minimize the strongly adaptive regret. We prove that our methods achieve near-optimal strongly adaptive regret for all interval lengths simultaneously. Experiments show that our methods consistently obtain better coverage and smaller prediction sets than existing methods on real-world tasks.
  arXiv  Detail & Related papers  (2023-02-15T18:59:30Z)
• Private Prediction Sets [72.75711776601973]
  Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
  arXiv  Detail & Related papers  (2021-02-11T18:59:11Z)
• Privacy Preserving Recalibration under Domain Shift [119.21243107946555]
  We introduce a framework that abstracts out the properties of recalibration problems under differential privacy constraints. We also design a novel recalibration algorithm, accuracy temperature scaling, that outperforms prior work on private datasets.
  arXiv  Detail & Related papers  (2020-08-21T18:43:37Z)
• AutoCP: Automated Pipelines for Accurate Prediction Intervals [84.16181066107984]
  This paper proposes an AutoML framework called Automatic Machine Learning for Conformal Prediction (AutoCP) Unlike the familiar AutoML frameworks that attempt to select the best prediction model, AutoCP constructs prediction intervals that achieve the user-specified target coverage rate. We tested AutoCP on a variety of datasets and found that it significantly outperforms benchmark algorithms.
  arXiv  Detail & Related papers  (2020-06-24T23:13:11Z)
• Structure-Adaptive Sequential Testing for Online False Discovery Rate Control [1.456699007803424]
  This work develops a new class of structure--adaptive sequential testing (SAST) rules for online false discover rate (FDR) control. A key element in our proposal is a new alpha--investment algorithm that precisely characterizes the gains and losses in sequential decision making.
  arXiv  Detail & Related papers  (2020-02-28T23:16:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.