{"id":9327,"date":"2025-12-24T07:03:29","date_gmt":"2025-12-24T07:03:29","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/24\/2512-19913\/"},"modified":"2025-12-24T07:03:29","modified_gmt":"2025-12-24T07:03:29","slug":"2512-19913","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/24\/2512-19913\/","title":{"rendered":"Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function"},"content":{"rendered":"

Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function
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arXiv:2512.19913v1 Announce Type: new
\nAbstract: We consider a generalization of the classifier-based density-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. The problem with most loss functions used for this task is that they implicitly define a relationship between the optimal classifier and the target quasiprobabilistic density ratio which is discontinuous or not surjective. We address these problems by introducing a convex loss function that is well-suited for both probabilistic and quasiprobabilistic density ratio estimation. To quantify performance, an extended version of the Sliced-Wasserstein distance is introduced which is compatible with quasiprobability distributions. We demonstrate our approach on a real-world example from particle physics, of di-Higgs production in association with jets via gluon-gluon fusion, and achieve state-of-the-art results.<\/div>\n

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\n Matthew Drnevich, Stephen Jiggins, Kyle Cranmer
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Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function arXiv:2512.19913v1 Announce Type: new Abstract: We consider a generalization of the classifier-based density-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. The problem with most loss functions used for this task is that they implicitly define a relationship between the […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,927,112],"tags":[1502,4493,2264],"class_list":["post-9327","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-hep-ex","category-stat-ml","tag-density","tag-quasiprobabilistic","tag-ratio"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9327"}],"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=9327"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9327\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9327"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9327"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9327"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}