{"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/accurate-uncertainties-for-deep-learning","title":"Accurate Uncertainties for Deep Learning Using Calibrated Regression","arxiv_id":"1807.00263","date":"2018-07-01","proceeding":"ICML 2018 7","authors":["Volodymyr Kuleshov","Nathan Fenner","Stefano Ermon"],"abstract":"Methods for reasoning under uncertainty are a key building block of accurate\nand reliable machine learning systems. Bayesian methods provide a general\nframework to quantify uncertainty. However, because of model misspecification\nand the use of approximate inference, Bayesian uncertainty estimates are often\ninaccurate -- for example, a 90% credible interval may not contain the true\noutcome 90% of the time. Here, we propose a simple procedure for calibrating\nany regression algorithm; when applied to Bayesian and probabilistic models, it\nis guaranteed to produce calibrated uncertainty estimates given enough data.\nOur procedure is inspired by Platt scaling and extends previous work on\nclassification. We evaluate this approach on Bayesian linear regression,\nfeedforward, and recurrent neural networks, and find that it consistently\noutputs well-calibrated credible intervals while improving performance on time\nseries forecasting and model-based reinforcement learning tasks.","url_abs":"http://arxiv.org/abs/1807.00263v1","url_pdf":"http://arxiv.org/pdf/1807.00263v1.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":"accurate-uncertainties-for-deep-learning","repo_url":"https://github.com/ulissigroup/uncertainty_benchmarking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"model-based-reinforcement-learning","task_name":"Model-based Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.00263","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}