{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T23:38:23Z","timestamp":1772321903426,"version":"3.50.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tpami.2023.3327300","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T17:51:51Z","timestamp":1698169911000},"page":"280-291","source":"Crossref","is-referenced-by-count":18,"title":["Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4091-7860","authenticated-orcid":false,"given":"Sangwoo","family":"Park","sequence":"first","affiliation":[{"name":"Department of Engineering, King's Communications, Learning & Information Processing (KCLIP) Lab, Centre for Intelligent Information Processing Systems (CIIPS), King's College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8041-3873","authenticated-orcid":false,"given":"Kfir M.","family":"Cohen","sequence":"additional","affiliation":[{"name":"Department of Engineering, King's Communications, Learning & Information Processing (KCLIP) Lab, Centre for Intelligent Information Processing Systems (CIIPS), King's College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9898-3209","authenticated-orcid":false,"given":"Osvaldo","family":"Simeone","sequence":"additional","affiliation":[{"name":"Department of Engineering, King's Communications, Learning & Information Processing (KCLIP) Lab, Centre for Intelligent Information Processing Systems (CIIPS), King's College London, London, U.K."}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-022-03410-x"},{"key":"ref2","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref3","article-title":"Averting a crisis in simulation-based inference","author":"Hermans","year":"2021","journal-title":"arXiv:2110.06581"},{"key":"ref4","first-page":"9537","article-title":"Probabilistic model-agnostic meta-learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Finn"},{"key":"ref5","first-page":"7343","article-title":"Bayesian model-agnostic meta-learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yoon"},{"key":"ref6","article-title":"Amortized Bayesian meta-learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi"},{"key":"ref7","first-page":"9758","article-title":"Information-theoretic analysis of epistemic uncertainty in Bayesian meta-learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Jose"},{"key":"ref8","first-page":"5479","article-title":"Learning under model misspecification: Applications to variational and ensemble methods","volume":"33","author":"Masegosa","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3071-5"},{"key":"ref10","first-page":"1613","article-title":"Weight uncertainty in neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Blundell"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/b106715"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1214\/20-AOS1965"},{"key":"ref13","first-page":"3329","article-title":"Few-shot conformal prediction with auxiliary tasks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fisch"},{"key":"ref14","article-title":"PAC prediction sets for meta-learning","author":"Park","year":"2022","journal-title":"arXiv:2207.02440"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2797022"},{"key":"ref17","first-page":"3581","article-title":"Classification with valid and adaptive coverage","volume":"33","author":"Romano","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref18","article-title":"Training uncertainty-aware classifiers with conformalized deep learning","author":"Einbinder","year":"2022","journal-title":"arXiv:2205.05878"},{"key":"ref19","article-title":"Uncertainty sets for image classifiers using conformal prediction","author":"Angelopoulos","year":"2020","journal-title":"arXiv: 2009.14193"},{"key":"ref20","article-title":"Uncertainty quantification over graph with conformalized graph neural networks","author":"Huang","year":"2023","journal-title":"arXiv:2305.14535"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2_8"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2017.2756630"},{"key":"ref23","article-title":"Matching networks for one shot learning","author":"Vinyals","year":"2016","journal-title":"arXiv:1606.04080"},{"key":"ref24","volume-title":"Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications","author":"Balasubramanian","year":"2014"},{"key":"ref25","article-title":"A primer on PAC-Bayesian learning","author":"Guedj","year":"2019","journal-title":"arXiv: 1901.05353"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1561\/2200000052"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1017\/9781009072205"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5355-6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12021"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1613\/jair.731"},{"key":"ref31","first-page":"205","article-title":"Meta-learning by adjusting priors based on extended PAC-Bayes theory","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Amit"},{"key":"ref32","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref33","first-page":"6861","article-title":"Differentiable ranking and sorting using optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cuturi"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.2307\/1913643"},{"key":"ref35","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref36","first-page":"8270","article-title":"PACm-Bayes: Narrowing the empirical risk gap in the misspecified Bayesian regime","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Morningstar"},{"key":"ref37","article-title":"How good is the bayes posterior in deep neural networks really?","author":"Wenzel","year":"2020","journal-title":"arXiv: 2002.02405"},{"key":"ref38","article-title":"Generalized variational inference: Three arguments for deriving new posteriors","author":"Knoblauch","year":"2019","journal-title":"arXiv: 1904.02063"},{"key":"ref39","volume-title":"Information Theory, Inference and Learning Algorithms","author":"MacKay","year":"2003"},{"key":"ref40","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref41","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093536"},{"key":"ref43","first-page":"280","article-title":"Meta-learning reliable priors in the function space","volume":"34","author":"Rothfuss","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref44","article-title":"Learning optimal conformal classifiers","author":"Stutz","year":"2021","journal-title":"arXiv:2110.09192"},{"key":"ref45","first-page":"3543","article-title":"Conformalized quantile regression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Romano"},{"key":"ref46","article-title":"CD-split and HPD-split: Efficient conformal regions in high dimensions","author":"Izbicki","year":"2020","journal-title":"arXiv: 2007.12778"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1093\/imaiai\/iaaa017"},{"key":"ref48","article-title":"MD-split+: Practical local conformal inference in high dimensions","author":"LeRoy","year":"2021","journal-title":"arXiv:2107.03280"},{"key":"ref49","first-page":"2530","article-title":"Conformal prediction under covariate shift","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tibshirani"},{"key":"ref50","first-page":"8378","article-title":"Locally valid and discriminative prediction intervals for deep learning models","volume":"34","author":"Lin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref51","article-title":"Conformal prediction with conditional guarantees","author":"Gibbs","year":"2023","journal-title":"arXiv:2305.12616"},{"key":"ref52","article-title":"Adaptive conformal regression with Jackknife+ rescaled scores","author":"Deutschmann","year":"2023","journal-title":"arXiv:2305.19901"},{"key":"ref53","article-title":"Alpha MAML: Adaptive model-agnostic meta-learning","author":"Behl","year":"2019","journal-title":"arXiv: 1905.07435"},{"key":"ref54","first-page":"1082","article-title":"On the convergence theory of gradient-based model-agnostic meta-learning algorithms","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Fallah"},{"key":"ref55","first-page":"7693","article-title":"Fast context adaptation via meta-learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zintgraf"},{"key":"ref56","article-title":"Recasting gradient-based meta-learning as hierarchical Bayes","author":"Grant","year":"2018","journal-title":"arXiv: 1801.08930"},{"key":"ref57","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref58","article-title":"Fast and accurate deep network learning by exponential linear units (ELUs)","author":"Clevert","year":"2015","journal-title":"arXiv:1511.07289"},{"key":"ref59","article-title":"Meta-learning without memorization","author":"Yin","year":"2019","journal-title":"arXiv: 1912.03820"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.23919\/eusipco54536.2021.9616270"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3043879"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref63","first-page":"2568","article-title":"One shot learning of simple visual concepts","volume-title":"Proc. Annu. Meeting Cogn. Sci. Soc.","author":"Lake"},{"key":"ref64","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi"},{"key":"ref65","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10345401\/10293257.pdf?arnumber=10293257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T01:22:18Z","timestamp":1703035338000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10293257\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":65,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3327300","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}