{"id":379,"date":"2024-12-05T07:03:18","date_gmt":"2024-12-05T07:03:18","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2024\/12\/05\/2412-03271\/"},"modified":"2024-12-05T07:03:18","modified_gmt":"2024-12-05T07:03:18","slug":"2412-03271","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2024\/12\/05\/2412-03271\/","title":{"rendered":"Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs"},"content":{"rendered":"

Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
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arXiv:2412.03271v1 Announce Type: new
\nAbstract: Neural Jump ODEs model the conditional expectation between observations by neural ODEs and jump at arrival of new observations. They have demonstrated effectiveness for fully data-driven online forecasting in settings with irregular and partial observations, operating under weak regularity assumptions. This work extends the framework to input-output systems, enabling direct applications in online filtering and classification. We establish theoretical convergence guarantees for this approach, providing a robust solution to $L^2$-optimal filtering. Empirical experiments highlight the model’s superior performance over classical parametric methods, particularly in scenarios with complex underlying distributions. These results emphasise the approach’s potential in time-sensitive domains such as finance and health monitoring, where real-time accuracy is crucial.<\/div>\n

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\n Jakob Heiss, Florian Krach, Thorsten Schmidt, F’elix B. Tambe-Ndonfack
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Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs arXiv:2412.03271v1 Announce Type: new Abstract: Neural Jump ODEs model the conditional expectation between observations by neural ODEs and jump at arrival of new observations. They have demonstrated effectiveness for fully data-driven online forecasting in settings with irregular and partial observations, operating under weak regularity assumptions. This […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,450,451,376,420,112],"tags":[452,453,118],"class_list":["post-379","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-cs-na","category-math-na","category-math-oc","category-math-pr","category-stat-ml","tag-filtering","tag-jump","tag-neural"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/379"}],"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=379"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/379\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=379"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=379"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}