# Conformal Selective Prediction with General Risk Control

arXiv: https://arxiv.org/abs/2603.24704 · HTML: https://mlsanity.com/paper/2603.24704/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 79 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation](https://mlsanity.com/paper/2302.09664/) (2023)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [Conformal Risk Control](https://mlsanity.com/paper/2208.02814/) (2022)
- [Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models](https://mlsanity.com/paper/2305.19187/) (2023)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://mlsanity.com/paper/2006.06138/) (2020)
- [Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement](https://mlsanity.com/paper/2407.18370/) (2024)
- [Hypothesis testing with e-values](https://mlsanity.com/paper/2410.23614/) (2024)
- [Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees](https://mlsanity.com/paper/2405.10301/) (2024)
- [Calibrated Selective Classification](https://mlsanity.com/paper/2208.12084/) (2022)
- [Model-free selective inference under covariate shift via weighted conformal p-values](https://mlsanity.com/paper/2307.09291/) (2023)
- [E-Values Expand the Scope of Conformal Prediction](https://mlsanity.com/paper/2503.13050/) (2025)
- [Enhancing Conformal Prediction Using E-Test Statistics](https://mlsanity.com/paper/2403.19082/) (2024)
- [Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization](https://mlsanity.com/paper/2411.17983/) (2024)
- [Measuring Evidence against Exchangeability and Group Invariance with E-values](https://mlsanity.com/paper/2310.01153/) (2023)
- [Selection from Hierarchical Data with Conformal e-values](https://mlsanity.com/paper/2501.02514/) (2025)
- [Diversifying Conformal Selections](https://mlsanity.com/paper/2506.16229/) (2025)
- [Conformalized Selective Regression](https://mlsanity.com/paper/2402.16300/) (2024)
- [Adaptive Coverage Policies in Conformal Prediction](https://mlsanity.com/paper/2510.04318/) (2025)
- [ACS: An interactive framework for conformal selection](https://mlsanity.com/paper/2507.15825/) (2025)
- [Online Conformal Selection with Accept-to-Reject Changes](https://mlsanity.com/paper/2508.13838/) (2025)
- [Fuzzy Prediction Sets: Conformal Prediction with E-values](https://mlsanity.com/paper/2509.13130/) (2025)
- [Model-agnostic Selective Labeling with Provable Statistical Guarantees](https://mlsanity.com/paper/2510.14581/) (2025)
- [Cross-Balancing for Data-Informed Design and Efficient Analysis of Observational Studies](https://mlsanity.com/paper/2511.15896/) (2025)
- [ConfHit: Conformal Generative Design with Oracle Free Guarantees](https://mlsanity.com/paper/2603.07371/) (2026)

## Built on by (most cited first, 12 of 12)

- [A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control](https://mlsanity.com/paper/2606.08517/) (2026)
- [Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables](https://mlsanity.com/paper/2604.11305/) (2026)
- [Calibrate What You SHIP: Post-Selection Risk Control for Verifier-Guided Text-to-Image Generation](https://mlsanity.com/paper/2608.21748/) (2026)
- [Safe to Stop? Risk-Constrained Stopping for Sequential Clinical Diagnosis Agents](https://mlsanity.com/paper/2609.09678/) (2026)
- [Robust Conformalized Selection with Noisy Responses](https://mlsanity.com/paper/2607.22985/) (2026)
- [Certify or Refuse: A Cross-Model Map for Selective Risk Control with Coverage Floors under Covariate Shift](https://mlsanity.com/paper/2608.10893/) (2026)
- [SafeRestore: Detector-Relative Risk Certificates for Selective Industrial Image Restoration](https://mlsanity.com/paper/2609.03475/) (2026)
- [Available Guardrails: Certifying Selective Prediction across ML Systems](https://mlsanity.com/paper/2609.22048/) (2026)
- [Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls](https://mlsanity.com/paper/2609.31384/) (2026)
- [Signed Lexical Confidence for Risk-Calibrated Intent Routing](https://mlsanity.com/paper/2610.00262/) (2026)
- [COVER: Learning to Accept More in Selective Sleep Staging](https://mlsanity.com/paper/2610.03911/) (2026)
- [Backward Conformal Prediction under Covariate Shift](https://mlsanity.com/paper/2610.16048/) (2026)

## Abstract (arXiv)

In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted. We propose Selective Conformal Risk control with E-values (SCoRE), a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk. SCoRE offers two types of guarantees on the risk among ``positive'' cases in which the system opts to trust the model. Built upon conformal inference and hypothesis testing ideas, SCoRE first constructs a class of (generalized) e-values, which are non-negative random variables whose product with the unknown risk has expectation no greater than one. Such a property is ensured by data exchangeability without requiring any modeling assumptions. Passing these e-values on to hypothesis testing procedures, we yield the binary trust decisions with finite-sample error control. SCoRE avoids the need of uniform concentration, and can be readily extended to settings with distribution shifts. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models.