{"id":5400,"date":"2025-04-24T11:49:23","date_gmt":"2025-04-24T09:49:23","guid":{"rendered":"https:\/\/www.hpc.mk\/?post_type=event_listing&#038;p=5400"},"modified":"2025-04-28T16:16:16","modified_gmt":"2025-04-28T14:16:16","slug":"22nd-international-conference-on-informatics-and-information-technologies-ciit-2025","status":"publish","type":"event_listing","link":"https:\/\/www.hpc.mk\/index.php\/event\/22nd-international-conference-on-informatics-and-information-technologies-ciit-2025\/","title":{"rendered":"22nd International Conference on Informatics and Information Technologies &#8211; CIIT 2025"},"content":{"rendered":"\n<p>The\u00a0<strong>International Conference on Informatics and Information Technologies<\/strong> is the 22nd of the series of conferences organized by the <a href=\"http:\/\/www.finki.ukim.mk\/\" target=\"_blank\" rel=\"noreferrer noopener\">Faculty of Computer Science and Engineering (FCSE)<\/a>. As part of the conference, the National Competence Center of North Macedonia, in collaboration with the HE ERA Chair AutoLearn-SI, HE MSCA PF AutoLLMSelect, and with support from the Slovenian AI Factory, has organized and will host the following presentations on April 25, 2025, starting at 16:30:<\/p>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Title: <strong>Leveraging Benchmarking Data for Automated Optimization<\/strong><br>Speaker: Tome Eftimov<br>Affiliation: Jozef Stefan Institute, Ljubljana, Slovenia<\/p>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>At the start of 2022, the evolutionary computation community published a call for action highlighting significant issues with metaphor-based metaheuristics in black-box optimization (BBO): useless metaphors, limited novelty, and biased experimental validation. This talk presents recent benchmarking advances for robust and reliable results and meta-learning approaches for algorithm selection. We focus on two methods: i) selecting representative data instances to generalize study findings, and ii) using algorithm footprints to identify easy or challenging problem instances based on landscape characteristics. Ultimately, the goal is a paradigm shift toward reducing resource waste and duplicated efforts, accelerating progress, and enabling effective automated algorithm configuration and selection through transferable insights.<\/p>\n\n\n\n<p>&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8211;<\/p>\n\n\n\n<p>Title: <strong>Robust and Interpretable Large Language Model Ranking Based on User Preferences<\/strong><br>Speaker: Ana Gjorgjevikj<br>Affiliation: Jozef Stefan institute, Ljubljana, Slovenia<\/p>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>This talk presents transparent benchmarking scenarios for large language models (LLMs), enabling users to evaluate models based on their specific needs, such as effectiveness, hardware constraints, and application demands. Using a well-established multi-criteria decision method, we generate benchmarking insights that reflect user preferences, assuming initial steps like benchmark dataset selection and LLM portfolio definition are already completed. LLMs are assessed across selected datasets by balancing performance and resource usage, with user feedback incorporated into performance metrics when relevant. Through two experiments\u2014one aggregating performance across datasets and another combining multiple metrics on a single dataset\u2014we show how user priorities influence interpretable and robust LLM rankings. This approach strengthens the relevance of benchmarking results and supports seamless integration into benchmarking platforms.<\/p>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><span style=\"box-sizing: inherit; font-weight: 600; color: rgb(17, 17, 17); font-family: Gilroy; font-size: 16px; white-space-collapse: collapse;\"><strong>Link for remote participants:<\/strong>&nbsp;<\/span><a href=\"http:\/\/\u041b\u0438\u043d\u043a \u0437\u0430 \u043e\u043d\u043b\u0430\u0458\u043d \u0443\u0447\u0435\u0441\u0442\u0432\u043e: www.teams.microsoft.com\" target=\"_blank\" rel=\"noreferrer noopener\">www.teams.microsoft.com<\/a><\/p>\n","protected":false},"featured_media":5401,"template":"","meta":[],"event_listing_category":[],"event_listing_type":[],"_links":{"self":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing\/5400"}],"collection":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing"}],"about":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/types\/event_listing"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/media\/5401"}],"wp:attachment":[{"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/media?parent=5400"}],"wp:term":[{"taxonomy":"event_listing_category","embeddable":true,"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing_category?post=5400"},{"taxonomy":"event_listing_type","embeddable":true,"href":"https:\/\/www.hpc.mk\/index.php\/wp-json\/wp\/v2\/event_listing_type?post=5400"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}