An official website of the United States government
Abstract Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice, data-driven methods are often used to identify specific test unit(s) of interest, requiring uncertainty quantification tailored to these focal units. In such cases, marginally valid conformal prediction intervals may fail to provide valid coverage for the focal unit(s) due to selection bias. This article presents a general framework for constructing a prediction set with finite-sample exact coverage, conditional on the unit being selected by a given procedure. The general form of our method accommodates arbitrary selection rules that are invariant to the permutation of the calibration units and generalizes Mondrian Conformal Prediction to multiple test units and non-equivariant classifiers. We also work out computationally efficient implementation of our framework for a number of realistic selection rules, including top-K selection, optimization-based selection, selection based on conformal p-values, and selection based on properties of preliminary conformal prediction sets. The performance of our methods is demonstrated via applications in drug discovery and health risk prediction.
more »
« less
This content will become publicly available on January 22, 2026
Leave-One-Out Stable Conformal Prediction
Conformal prediction (CP) is an important tool for distribution-free predictive uncertainty quantification. Yet, a major challenge is to balance computational efficiency and prediction accuracy, particularly for multiple predictions. We propose Leave-One-Out Stable Conformal Prediction (LOO-StabCP), a novel method to speed up full conformal using algorithmic stability without sample splitting. By leveraging leave-one-out stability, our method is much faster in handling a large number of prediction requests compared to existing method RO-StabCP based on replace-one stability. We derived stability bounds for several popular machine learning tools: regularized loss minimization (RLM) and stochastic gradient descent (SGD), as well as kernel method, neural networks and bagging. Our method is theoretically justified and demonstrates superior numerical performance on synthetic and real-world data. We applied our method to a screening problem, where its effective exploitation of training data led to improved test power compared to state-of-the-art method based on split conformal.
more »
« less
- Award ID(s):
- 2311109
- PAR ID:
- 10574800
- Publisher / Repository:
- The Thirteenth International Conference on Learning Representations
- Date Published:
- Subject(s) / Keyword(s):
- Conformal Prediction, Algorithmic Stability, Regularized Loss Minimization, Stochastic Gradient Descent
- Format(s):
- Medium: X
- Location:
- https://openreview.net/forum?id=Bt1vnCnAVS
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We give a simple, generic conformal prediction method for sequential prediction that achieves target empirical coverage guarantees against adversarially chosen data. It is computationally lightweight -- comparable to split conformal prediction -- but does not require having a held-out validation set, and so all data can be used for training models from which to derive a conformal score. It gives stronger than marginal coverage guarantees in two ways. First, it gives threshold calibrated prediction sets that have correct empirical coverage even conditional on the threshold used to form the prediction set from the conformal score. Second, the user can specify an arbitrary collection of subsets of the feature space -- possibly intersecting -- and the coverage guarantees also hold conditional on membership in each of these subsets. We call our algorithm MVP, short for MultiValid Prediction. We give both theory and an extensive set of empirical evaluations.more » « less
-
Hand signals are the most widely used, feasible, and device-free communication method in manufacturing plants, airport ramps, and other noisy or voice-prohibiting environments. Enabling IoT agents, such as robots, to recognize and communicate by hand signals will facilitate human-machine collaboration for the emerging “Industry 5.0.” While many prior works succeed in hand signal recognition, few can rigorously guarantee the accuracy of their predictions. This project proposes a method that builds on the theory of conformal prediction (CP) to provide statistical guarantees on hand signal recognition accuracy and, based on it, measure the uncertainty in this communication process. It utilizes a calibration set with a few representative samples to ensure that trained models provide a conformal prediction set that reaches or exceeds the truth worth and trustworthiness at a user-specified level. Subsequently, the uncertainty in the recognition process can be detected by measuring the length of the conformal prediction set. Furthermore, the proposed CP-based method can be used with IoT models without fine-tuning as an out-of-the-box and promising lightweight approach to modeling uncertainty. Our experiments show that the proposed conformal recognition method can achieve accurate hand signal prediction in novel scenarios. When selecting an error level α = 0.10, it provided 100% accuracy for out-of-distribution test sets.more » « less
-
ABSTRACT Conformal predictions transform a measurable, heuristic notion of uncertainty into statistically valid confidence intervals such that, for a future sample, the true class prediction will be included in the conformal prediction set at a predetermined confidence. In a Bayesian perspective, common estimates of uncertainty in multivariate classification, namelyp‐values, only provide the probability that the data fits the presumed class model,P(D|M). Conformal predictions, on the other hand, address the more meaningful probability that a model fits the data,P(M|D). Herein, two methods to perform inductive conformal predictions are investigated—the traditional Split Conformal Prediction that uses an external calibration set and a novel Bagged Conformal Prediction, closely related to Cross Conformal Predictions, that utilizes bagging to calibrate the heuristic notions of uncertainty. Methods for preprocessing the conformal prediction scores to improve performance are discussed and investigated. These conformal prediction strategies are applied to identifying four non‐steroidal anti‐inflammatory drugs (NSAIDs) from hyperspectral Raman imaging data. In addition to assigning meaningful confidence intervals on the model results, we herein demonstrate how conformal predictions can add additional diagnostics for model quality and method stability.more » « less
-
Tremor is one of the main symptoms of Parkinson’s Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients’ tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients’ tremor from continuous monitoring of the subjects’ movement in their natural environment.more » « less
