{"url":"/task/conformal-prediction","name":"Conformal Prediction","slug":"conformal-prediction","description_markdown":"Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":704,"papers_with_code":277,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":277,"tagged_in_all":704,"items":[{"url":"/paper/uncertainty-sets-for-image-classifiers-using","title":"Uncertainty Sets for Image Classifiers using Conformal Prediction","date":"2020-09-29","arxiv_id":"2009.14193","repositories_listed":5,"syntology":{"n":63,"n_ran":19,"n_unverified":44,"n_pointer_only":19}},{"url":"/paper/conformalized-quantile-regression","title":"Conformalized Quantile Regression","date":"2019-05-08","arxiv_id":"1905.03222","repositories_listed":5,"syntology":null},{"url":"/paper/a-gentle-introduction-to-conformal-prediction","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","date":"2021-07-15","arxiv_id":"2107.07511","repositories_listed":4,"syntology":{"n":12,"n_ran":0,"n_unverified":12,"n_pointer_only":0}},{"url":"/paper/large-language-model-validity-via-enhanced","title":"Large language model validity via enhanced conformal prediction 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Diverse Semifactual Explanations of Reject","date":"2022-07-05","arxiv_id":"2207.01898","repositories_listed":2,"syntology":{"n":8,"n_ran":0,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/conformal-prediction-set-for-time-series","title":"Conformal prediction set for time-series","date":"2022-06-15","arxiv_id":"2206.07851","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":2}},{"url":"/paper/adaptive-conformal-predictions-for-time","title":"Adaptive Conformal Predictions for Time Series","date":"2022-02-15","arxiv_id":"2202.07282","repositories_listed":2,"syntology":{"n":11,"n_ran":0,"n_unverified":11,"n_pointer_only":0}},{"url":"/paper/image-to-image-regression-with-distribution","title":"Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging","date":"2022-02-10","arxiv_id":"2202.05265","repositories_listed":2,"syntology":{"n":21,"n_ran":2,"n_unverified":19,"n_pointer_only":0}},{"url":"/paper/learning-optimal-conformal-classifiers-1","title":"Learning Optimal Conformal Classifiers","date":"2021-10-18","arxiv_id":"2110.09192","repositories_listed":2,"syntology":{"n":24,"n_ran":1,"n_unverified":23,"n_pointer_only":3}},{"url":"/paper/conformalized-survival-analysis","title":"Conformalized Survival Analysis","date":"2021-03-17","arxiv_id":"2103.09763","repositories_listed":2,"syntology":null},{"url":"/paper/conformal-prediction-interval-for-dynamic","title":"Conformal prediction interval for dynamic time-series","date":"2020-10-18","arxiv_id":"2010.09107","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/the-trilemma-of-truth-in-large-language","title":"The Trilemma of Truth in Large Language Models","date":"2025-06-30","arxiv_id":"2506.23921","repositories_listed":1,"syntology":null},{"url":"/paper/response-quality-assessment-for-retrieval","title":"Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality","date":"2025-06-26","arxiv_id":"2506.20978","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":3}},{"url":"/paper/certdw-towards-certified-dataset-ownership","title":"CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction","date":"2025-06-16","arxiv_id":"2506.13160","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-adversarial-robustness-with","title":"Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability","date":"2025-06-09","arxiv_id":"2506.07804","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-robust-conformal-prediction-via","title":"Efficient Robust Conformal Prediction via 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