{"id":6579,"date":"2021-12-01T12:43:22","date_gmt":"2021-12-01T12:43:22","guid":{"rendered":"https:\/\/www.digitalfutures.kth.se\/?page_id=6579"},"modified":"2022-04-15T16:45:45","modified_gmt":"2022-04-15T16:45:45","slug":"henrik-bostrom","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/digital-futures-fellows\/henrik-bostrom\/","title":{"rendered":"Henrik Bostr\u00f6m"},"content":{"rendered":"
\n

Title of the project
\n<\/i>Reliable Machine Learning<\/span><\/span>\u00a0<\/span><\/strong><\/p>\n

Background<\/span>\u00a0<\/span>and summary of fellowship<\/span>:
\n<\/span><\/span><\/em>In many application areas, it is not sufficient to present the output<\/span>\u00a0<\/span>of\u00a0machine learning models to the users without providing any<\/span>\u00a0<\/span>information\u00a0on what leads to the specific predictions or<\/span>\u00a0<\/span>recommendations\u00a0and how (un)certain they are. The strongest machine<\/span>\u00a0<\/span>learning\u00a0models are however often essentially black boxes. In order to<\/span>\u00a0<\/span>enable\u00a0trust in such models, techniques for explaining the predictions<\/span>\u00a0<\/span>in\u00a0the form of interpretable approximations are currently being<\/span>\u00a0<\/span>investigated. Another cornerstone for enabling trust is that the<\/span>\u00a0<\/span>uncertainty\u00a0of the output of the machine learning models is properly<\/span>\u00a0<\/span>quantified, e.g., that the output prediction intervals or probability<\/span>\u00a0<\/span>distributions\u00a0are well-calibrated.<\/span>\u00a0<\/span><\/p>\n

Motivated by collaborations <\/span>with<\/span>\u00a0<\/span>Karolinska\u00a0Institutet\/University hospital on sepsis prediction,\u00a0<\/span>Scania on predictive maintenance and <\/span>the Swedish National Financial\u00a0Management Authority on gross domestic product (GDP) forecasting, techniques for quantifying<\/span>\u00a0<\/span>uncertainty\u00a0and explaining predictions <\/span>will be developed and evaluated. In addition to<\/span>\u00a0<\/span>scientific\u00a0papers, the output of the project will be Python packages<\/span>\u00a0<\/span>to\u00a0support reliable machine learning, enabling<\/span>\u00a0<\/span>predictions\u00a0of state-of-the-art machine learning models to be<\/span>\u00a0<\/span>complemented\u00a0with explanations and uncertainty quantification.<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Reliable Machine Learning<\/p>\n","protected":false},"author":46,"featured_media":0,"parent":6563,"menu_order":184,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-6579","page","type-page","status-publish","hentry"],"yoast_head":"\nHenrik Bostr\u00f6m — Digital Futures<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Henrik Bostr\u00f6m — Digital Futures\" \/>\n<meta property=\"og:description\" content=\"Reliable Machine Learning\" \/>\n<meta property=\"og:url\" content=\"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/digital-futures-fellows\/henrik-bostrom\/\" \/>\n<meta property=\"og:site_name\" content=\"Digital Futures\" \/>\n<meta property=\"article:modified_time\" 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