{"id":5200,"date":"2025-07-10T07:02:50","date_gmt":"2025-07-10T07:02:50","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/10\/2507-06867\/"},"modified":"2025-07-10T07:02:50","modified_gmt":"2025-07-10T07:02:50","slug":"2507-06867","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/10\/2507-06867\/","title":{"rendered":"Conformal Prediction for Long-Tailed Classification"},"content":{"rendered":"

Conformal Prediction for Long-Tailed Classification
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arXiv:2507.06867v1 Announce Type: new
\nAbstract: Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii) be a reasonable size, allowing users to easily verify candidate labels. Unfortunately, existing conformal prediction methods, when applied to the long-tailed setting, force practitioners to make a binary choice between small sets with poor class-conditional coverage or sets with very good class-conditional coverage but that are extremely large. We propose methods with guaranteed marginal coverage that smoothly trade off between set size and class-conditional coverage. First, we propose a conformal score function, prevalence-adjusted softmax, that targets a relaxed notion of class-conditional coverage called macro-coverage. Second, we propose a label-weighted conformal prediction method that allows us to interpolate between marginal and class-conditional conformal prediction. We demonstrate our methods on Pl@ntNet and iNaturalist, two long-tailed image datasets with 1,081 and 8,142 classes, respectively.<\/div>\n

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\n Tiffany Ding, Jean-Baptiste Fermanian, Joseph Salmon
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Conformal Prediction for Long-Tailed Classification arXiv:2507.06867v1 Announce Type: new Abstract: Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii) […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,611,113,183,112],"tags":[3184,1610,121],"class_list":["post-5200","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-cv","category-cs-lg","category-stat-me","category-stat-ml","tag-class","tag-coverage","tag-prediction"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5200"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=5200"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5200\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5200"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5200"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5200"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}