{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/probabilistic-matrix-factorization-for","title":"Probabilistic Matrix Factorization for Automated Machine Learning","arxiv_id":"1705.05355","date":"2017-05-15","proceeding":"NeurIPS 2018 12","authors":["Nicolo Fusi","Rishit Sheth","Huseyn Melih Elibol"],"abstract":"In order to achieve state-of-the-art performance, modern machine learning\ntechniques require careful data pre-processing and hyperparameter tuning.\nMoreover, given the ever increasing number of machine learning models being\ndeveloped, model selection is becoming increasingly important. Automating the\nselection and tuning of machine learning pipelines consisting of data\npre-processing methods and machine learning models, has long been one of the\ngoals of the machine learning community. In this paper, we tackle this\nmeta-learning task by combining ideas from collaborative filtering and Bayesian\noptimization. Using probabilistic matrix factorization techniques and\nacquisition functions from Bayesian optimization, we exploit experiments\nperformed in hundreds of different datasets to guide the exploration of the\nspace of possible pipelines. In our experiments, we show that our approach\nquickly identifies high-performing pipelines across a wide range of datasets,\nsignificantly outperforming the current state-of-the-art.","url_abs":"http://arxiv.org/abs/1705.05355v2","url_pdf":"http://arxiv.org/pdf/1705.05355v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"probabilistic-matrix-factorization-for","repo_url":"https://github.com/rsheth80/pmf-automl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.05355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.05355"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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