{"id":2255,"date":"2026-09-21T09:21:53","date_gmt":"2026-09-21T07:21:53","guid":{"rendered":"https:\/\/www.ideas.edu.pl\/?post_type=publikacje&#038;p=2255"},"modified":"2026-09-21T09:21:54","modified_gmt":"2026-09-21T07:21:54","slug":"hyconex-hypernetwork-classifier-with-counterfactual-explanations-for-tabular-data","status":"publish","type":"publikacje","link":"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hyconex-hypernetwork-classifier-with-counterfactual-explanations-for-tabular-data\/","title":{"rendered":"HyConEx: A hypernetwork classifier with counterfactual explanations for tabular data"},"content":{"rendered":"<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">In recent years, there has been growing interest in explainable AI methods. In addition to making accurate predictions, we also want to understand what the model\u2019s decision is based on. One of the fundamental levels of interpretability is to provide counterfactual examples that explain the rationale behind the decision and identify which features\u2014and to what extent\u2014must be modified to alter the model\u2019s outcome. To address these requirements, we introduce HyConEx, a classification model based on deep hypernetworks specifically designed for tabular data. Thanks to its unique architecture, HyConEx not only provides class predictions but also delivers local interpretations for individual data samples in the form of counterfactual examples that steer a given sample toward an alternative class. While many explainable methods generate counterfactuals for external models, no interpretable classifiers have yet been developed that simultaneously produce counterfactual samples. HyConEx achieves competitive performance on several metrics assessing classification accuracy and meets the criteria for a proper counterfactual attack. This makes HyConEx a distinctive deep learning model that combines predictions and explainers into a single, all-in-one neural network.<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Authors: Patryk Marsza\u0142ek, Ulvi Movsum-zada, Oleksii Furman, Kamil Ksi\u0105\u017cek, Przemys\u0142aw Spurek, Marek \u015amieja<\/p>\n\n\n\n<div style=\"height:64px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-group is-layout-grid wp-container-core-group-is-layout-5ab56a3c wp-block-group-is-layout-grid\"><\/div>\n\n\n\n<div class=\"wp-block-group is-layout-grid wp-container-core-group-is-layout-5ab56a3c wp-block-group-is-layout-grid\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>","protected":false},"template":"","nazwa-konferencji":[],"rodzaj-publikacji":[38],"rok-publikacji":[27],"class_list":["post-2255","publikacje","type-publikacje","status-publish","hentry","rodzaj-publikacji-artykul-w-czasopismie","rok-publikacji-27"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data &#8226; IDEAS Instytut Badawczy<\/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:\/\/www.ideas.edu.pl\/en\/publikacje\/hyconex-hypernetwork-classifier-with-counterfactual-explanations-for-tabular-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data &#8226; IDEAS Instytut Badawczy\" \/>\n<meta property=\"og:description\" content=\"In recent years, there has been a growing interest in explainable AI methods. In addition to making accurate predictions, we also want to understand what the model\u2019s decision is based on. 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