{"id":4546,"date":"2023-05-16T08:30:47","date_gmt":"2023-05-16T08:30:47","guid":{"rendered":"https:\/\/isipta23.sipta.org\/?page_id=4546"},"modified":"2023-07-13T07:48:54","modified_gmt":"2023-07-13T07:48:54","slug":"long-rodemann","status":"publish","type":"page","link":"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/","title":{"rendered":"Long Rodemann"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"4546\" class=\"elementor elementor-4546\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t\t\t\t<section data-eae-particle=\"{\n  &quot;particles&quot;: {\n    &quot;number&quot;: {\n      &quot;value&quot;: 80,\n      &quot;density&quot;: {\n        &quot;enable&quot;: true,\n        &quot;value_area&quot;: 800\n      }\n    },\n    &quot;color&quot;: {\n      &quot;value&quot;: &quot;#ffffff&quot;\n    },\n    &quot;shape&quot;: {\n      &quot;type&quot;: &quot;circle&quot;,\n      &quot;stroke&quot;: {\n        &quot;width&quot;: 0,\n        &quot;color&quot;: &quot;#000000&quot;\n      },\n      &quot;polygon&quot;: {\n        &quot;nb_sides&quot;: 5\n      },\n      &quot;image&quot;: {\n        &quot;src&quot;: &quot;img\/github.svg&quot;,\n        &quot;width&quot;: 100,\n        &quot;height&quot;: 100\n      }\n    },\n    &quot;opacity&quot;: {\n      &quot;value&quot;: 0.5,\n      &quot;random&quot;: false,\n      &quot;anim&quot;: {\n        &quot;enable&quot;: false,\n        &quot;speed&quot;: 1,\n        &quot;opacity_min&quot;: 0.1,\n        &quot;sync&quot;: false\n      }\n    },\n    &quot;size&quot;: {\n      &quot;value&quot;: 3,\n      &quot;random&quot;: true,\n      &quot;anim&quot;: {\n        &quot;enable&quot;: false,\n        &quot;speed&quot;: 40,\n        &quot;size_min&quot;: 0.1,\n        &quot;sync&quot;: false\n      }\n    },\n    &quot;line_linked&quot;: {\n      &quot;enable&quot;: true,\n      &quot;distance&quot;: 150,\n      &quot;color&quot;: &quot;#ffffff&quot;,\n      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data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;shape_divider_bottom&quot;:&quot;wave-brush&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t<div class=\"elementor-shape elementor-shape-bottom\" data-negative=\"false\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 283.5 27.8\" preserveAspectRatio=\"none\">\n\t<path class=\"elementor-shape-fill\" d=\"M283.5,9.7c0,0-7.3,4.3-14,4.6c-6.8,0.3-12.6,0-20.9-1.5c-11.3-2-33.1-10.1-44.7-5.7\ts-12.1,4.6-18,7.4c-6.6,3.2-20,9.6-36.6,9.3C131.6,23.5,99.5,7.2,86.3,8c-1.4,0.1-6.6,0.8-10.5,2c-3.8,1.2-9.4,3.8-17,4.7\tc-3.2,0.4-8.3,1.1-14.2,0.9c-1.5-0.1-6.3-0.4-12-1.6c-5.7-1.2-11-3.1-15.8-3.7C6.5,9.2,0,10.8,0,10.8V0h283.5V9.7z M260.8,11.3\tc-0.7-1-2-0.4-4.3-0.4c-2.3,0-6.1-1.2-5.8-1.1c0.3,0.1,3.1,1.5,6,1.9C259.7,12.2,261.4,12.3,260.8,11.3z 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elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cd1ef36 elementor-widget elementor-widget-image\" data-id=\"cd1ef36\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"128\" src=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-1024x128.png\" class=\"attachment-large size-large wp-image-546\" alt=\"LogoISIPTA23-long-white\" srcset=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-1024x128.png 1024w, https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-300x38.png 300w, https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-768x96.png 768w, https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white.png 1284w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-017a3c3 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"017a3c3\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 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.elementor-divider__element{margin-right:0}.elementor-widget-divider:not(.elementor-widget-divider--view-line_text):not(.elementor-widget-divider--view-line_icon) .elementor-divider-separator{border-top:var(--divider-border-width) var(--divider-border-style) var(--divider-color)}.elementor-widget-divider--separator-type-pattern{--divider-border-style:none}.elementor-widget-divider--separator-type-pattern.elementor-widget-divider--view-line .elementor-divider-separator,.elementor-widget-divider--separator-type-pattern:not(.elementor-widget-divider--view-line) .elementor-divider-separator:after,.elementor-widget-divider--separator-type-pattern:not(.elementor-widget-divider--view-line) .elementor-divider-separator:before,.elementor-widget-divider--separator-type-pattern:not([class*=elementor-widget-divider--view]) .elementor-divider-separator{width:100%;min-height:var(--divider-pattern-height);-webkit-mask-size:var(--divider-pattern-size) 100%;mask-size:var(--divider-pattern-size) 100%;-webkit-mask-repeat:var(--divider-pattern-repeat);mask-repeat:var(--divider-pattern-repeat);background-color:var(--divider-color);-webkit-mask-image:var(--divider-pattern-url);mask-image:var(--divider-pattern-url)}.elementor-widget-divider--no-spacing{--divider-pattern-size:auto}.elementor-widget-divider--bg-round{--divider-pattern-repeat:round}.rtl .elementor-widget-divider .elementor-divider__text{direction:rtl}.e-con-inner>.elementor-widget-divider,.e-con>.elementor-widget-divider{width:var(--container-widget-width,100%);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9adf215 elementor-widget elementor-widget-heading\" data-id=\"9adf215\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Rodemann, Jansen, Schollmeyer and Augustin<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fac39ee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fac39ee\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-daa0aae\" data-id=\"daa0aae\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b4d9d71 elementor-widget elementor-widget-text-editor\" data-id=\"b4d9d71\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 20-09-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<a class=\"boton-volver\" href=\"javascript:history.back()\">\u2aa1 Back<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-99180d5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"99180d5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1cf27c4\" data-id=\"1cf27c4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-63d4f60 elementor-widget elementor-widget-heading\" data-id=\"63d4f60\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">In All Likelihoods: Robust Selection of Pseudo-Labeled Data<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56b42ba elementor-widget elementor-widget-heading\" data-id=\"56b42ba\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<span class=\"elementor-heading-title elementor-size-default\">Self-training is a simple yet effective method within semi-supervised learning. Self-training's rationale is to iteratively enhance training data by adding pseudo-labeled data. Its generalization performance heavily depends on the selection of these pseudo-labeled data (PLS). In this paper, we render PLS more robust towards the involved modeling assumptions. To this end, we treat PLS as a decision problem, which allows us to introduce a generalized utility function. The idea is to select pseudo-labeled data that maximize a multi-objective utility function. We demonstrate that the latter can be constructed to account for different sources of uncertainty and explore three examples: model selection, accumulation of errors and covariate shift. In the absence of second-order information on such uncertainties, we furthermore consider the generic approach of the generalized Bayesian <span class=\"katex-eq\" data-katex-display=\"false\">\\alpha<\/span>-cut updating rule for credal sets. We spotlight the application of three of our robust extensions on both simulated and three real-world data sets. In a benchmarking study, we compare these extensions to traditional PLS methods. Results suggest that robustness with regard to model choice can lead to substantial accuracy gains.<\/span>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-48bccb1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"48bccb1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-d0c021d\" data-id=\"d0c021d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-14561db oew-align-center elementor-widget elementor-widget-oew-button-effects\" data-id=\"14561db\" data-element_type=\"widget\" data-widget_type=\"oew-button-effects.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\r\n\r\n\t\t<div class=\"oew-button oew-btn-effect-30\">\r\n\t\t\t<a href=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2023\/06\/rodemann23a.pdf\" class=\"oew-link\">\r\n\t\t\t\t\r\n\t\t\t\t<span>PAPER<\/span>\r\n\r\n\t\t\t\t\t\t\t<\/a>\r\n\t\t<\/div>\r\n\r\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c4fecaa\" data-id=\"c4fecaa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f0fba04 oew-align-center elementor-widget elementor-widget-oew-button-effects\" data-id=\"f0fba04\" data-element_type=\"widget\" data-widget_type=\"oew-button-effects.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\r\n\r\n\t\t<div class=\"oew-button oew-btn-effect-30\">\r\n\t\t\t<a href=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2023\/07\/5Rodemann.pdf\" class=\"oew-link\">\r\n\t\t\t\t\r\n\t\t\t\t<span>PRESENTATION<\/span>\r\n\r\n\t\t\t\t\t\t\t<\/a>\r\n\t\t<\/div>\r\n\r\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-0d19e5b\" data-id=\"0d19e5b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b8fd350 oew-align-center elementor-widget elementor-widget-oew-button-effects\" data-id=\"b8fd350\" data-element_type=\"widget\" data-widget_type=\"oew-button-effects.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\r\n\r\n\t\t<div class=\"oew-button oew-btn-effect-30\">\r\n\t\t\t<a href=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2023\/07\/rodemann-poster.pdf\" class=\"oew-link\">\r\n\t\t\t\t\r\n\t\t\t\t<span>POSTER<\/span>\r\n\r\n\t\t\t\t\t\t\t<\/a>\r\n\t\t<\/div>\r\n\r\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dd2b88d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dd2b88d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e2b8390\" data-id=\"e2b8390\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e9aa4b4 elementor-widget elementor-widget-text-editor\" data-id=\"e9aa4b4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<a class=\"boton-volver\" href=\"javascript:history.back()\">\u2aa1 Back<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Rodemann, Jansen, Schollmeyer and Augustin \u2aa1 Back In All Likelihoods: Robust Selection of Pseudo-Labeled Data Self-training is a simple yet effective method within semi-supervised learning. Self-training&#8217;s rationale is to iteratively enhance training data by adding pseudo-labeled data. Its generalization performance heavily depends on the selection of these pseudo-labeled data (PLS). In this paper, we render [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":3821,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Long Rodemann - ISIPTA 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Long Rodemann - ISIPTA 2023\" \/>\n<meta property=\"og:description\" content=\"Rodemann, Jansen, Schollmeyer and Augustin \u2aa1 Back In All Likelihoods: Robust Selection of Pseudo-Labeled Data Self-training is a simple yet effective method within semi-supervised learning. Self-training&#8217;s rationale is to iteratively enhance training data by adding pseudo-labeled data. Its generalization performance heavily depends on the selection of these pseudo-labeled data (PLS). In this paper, we render [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/\" \/>\n<meta property=\"og:site_name\" content=\"ISIPTA 2023\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-13T07:48:54+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/LogoISIPTA23-long-white-1024x128.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/\",\"url\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/\",\"name\":\"Long Rodemann - ISIPTA 2023\",\"isPartOf\":{\"@id\":\"https:\/\/isipta23.sipta.org\/#website\"},\"datePublished\":\"2023-05-16T08:30:47+00:00\",\"dateModified\":\"2023-07-13T07:48:54+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/#breadcrumb\"},\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Portada\",\"item\":\"https:\/\/isipta23.sipta.org\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Accepted Papers\",\"item\":\"https:\/\/isipta23.sipta.org\/accepted-papers\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Long Rodemann\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/isipta23.sipta.org\/#website\",\"url\":\"https:\/\/isipta23.sipta.org\/\",\"name\":\"ISIPTA 2023\",\"description\":\"The International Symposium on Imprecise Probabilities\",\"publisher\":{\"@id\":\"https:\/\/isipta23.sipta.org\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/isipta23.sipta.org\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/isipta23.sipta.org\/#organization\",\"name\":\"ISIPTA 2023\",\"url\":\"https:\/\/isipta23.sipta.org\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/isipta23.sipta.org\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/cropped-LogoISIPTA23-white.png\",\"contentUrl\":\"https:\/\/isipta23.sipta.org\/wp-content\/uploads\/2022\/08\/cropped-LogoISIPTA23-white.png\",\"width\":500,\"height\":500,\"caption\":\"ISIPTA 2023\"},\"image\":{\"@id\":\"https:\/\/isipta23.sipta.org\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Long Rodemann - ISIPTA 2023","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/isipta23.sipta.org\/accepted-papers\/long-rodemann\/","og_locale":"en_GB","og_type":"article","og_title":"Long Rodemann - ISIPTA 2023","og_description":"Rodemann, Jansen, Schollmeyer and Augustin \u2aa1 Back In All Likelihoods: Robust Selection of Pseudo-Labeled Data Self-training is a simple yet effective method within semi-supervised learning. 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