{"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/decomposition-of-uncertainty-in-bayesian-deep","title":"Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning","arxiv_id":"1710.07283","date":"2017-10-19","proceeding":"ICML 2018 7","authors":["Stefan Depeweg","José Miguel Hernández-Lobato","Finale Doshi-Velez","Steffen Udluft"],"abstract":"Bayesian neural networks with latent variables are scalable and flexible\nprobabilistic models: They account for uncertainty in the estimation of the\nnetwork weights and, by making use of latent variables, can capture complex\nnoise patterns in the data. We show how to extract and decompose uncertainty\ninto epistemic and aleatoric components for decision-making purposes. This\nallows us to successfully identify informative points for active learning of\nfunctions with heteroscedastic and bimodal noise. Using the decomposition we\nfurther define a novel risk-sensitive criterion for reinforcement learning to\nidentify policies that balance expected cost, model-bias and noise aversion.","url_abs":"http://arxiv.org/abs/1710.07283v4","url_pdf":"http://arxiv.org/pdf/1710.07283v4.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":"decomposition-of-uncertainty-in-bayesian-deep","repo_url":"https://github.com/lightning-uq-box/lightning-uq-box","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.07283","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}