21 Station Road
\nCambridge
\nCB1 2FB<\/p>\n
<\/p>\n
View the whole series on talks.cam (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n Frontiers in Artificial Intelligence<\/span> is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial intelligence. Students, scientists, and engineers in academia and industry are all welcome to join us for these exciting talks and the opportunity to socialize with the Cambridge AI\/ML community.<\/p>\n With the increasing integration of machine learning into real systems, it is crucial that trained models are optimized to reflect real-world tradeoffs. Increasing interest in proper evaluation has led to a wide variety of metrics employed in practice, often specially designed by experts. However, modern training strategies have not kept up with the explosion of metrics, leaving practitioners to resort to heuristics. To address this shortcoming, I will present a simple, yet consistent post-processing rule which improves the performance of trained binary, multilabel, and multioutput classifiers. Building on these results, I will propose a framework for metric elicitation, which addresses the broader question of how one might select an evaluation metric for real world problems so that it reflects true preferences.Opens in a new tab<\/span><\/p>\n Tal Ben-Nun – Neural Code Comprehension: A Learnable Representation of Code Semantics<\/a><\/p>\n Hoda Heidari \u2013 What can Fair ML learn from Economic Theories of Disruptive Justice?<\/a><\/p>\n Finale Doshi-Velez \u2013 Interpretability in Machine Learning: What it means, How we\u2019re getting there. (opens in new tab)<\/span><\/a><\/p>\n<\/div>\n<\/div>\n Francis Bach – Optimal algorithms for smooth and strongly convex distributed optimization in networks<\/a><\/p>\n Francesco Orabona – Coin Betting for Backprop without Learning rates and More<\/a><\/p>\n Regina Barzilay – How Can NLP Help Cure Cancer? (opens in new tab)<\/span><\/a><\/p>\n Aapo Hyvarinen – Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning\u00a0<\/a><\/p>\n Max Welling – Generalizing Convolutions for Deep Learning <\/a><\/p>\n Opens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" 21 Station Road Cambridge CB1 2FB View the whole series on talks.cam (opens in new tab)Opens in a new tab Frontiers in Artificial Intelligence is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2019-07-31","msr_enddate":"2019-07-31","msr_location":"MSR Cambridge","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"11:00-12:00","msr_hide_region":true,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[239178],"msr-event-type":[197944],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-600501","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-region-europe","msr-event-type-hosted-by-microsoft","msr-locale-en_us"],"msr_about":"\n\n 21 Station Road <\/p>\n View the whole series on talks.cam (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n Frontiers in Artificial Intelligence<\/span> is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial intelligence. Students, scientists, and engineers in academia and industry are all welcome to join us for these exciting talks and the opportunity to socialize with the Cambridge AI\/ML community.<\/p>\n With the increasing integration of machine learning into real systems, it is crucial that trained models are optimized to reflect real-world tradeoffs. Increasing interest in proper evaluation has led to a wide variety of metrics employed in practice, often specially designed by experts. However, modern training strategies have not kept up with the explosion of metrics, leaving practitioners to resort to heuristics. To address this shortcoming, I will present a simple, yet consistent post-processing rule which improves the performance of trained binary, multilabel, and multioutput classifiers. Building on these results, I will propose a framework for metric elicitation, which addresses the broader question of how one might select an evaluation metric for real world problems so that it reflects true preferences.Opens in a new tab<\/span><\/p>\n Tal Ben-Nun – Neural Code Comprehension: A Learnable Representation of Code Semantics<\/a><\/p>\n Hoda Heidari \u2013 What can Fair ML learn from Economic Theories of Disruptive Justice?<\/a><\/p>\n Finale Doshi-Velez \u2013 Interpretability in Machine Learning: What it means, How we\u2019re getting there.<\/a><\/p>\n<\/div>\n<\/div>\n Francis Bach – Optimal algorithms for smooth and strongly convex distributed optimization in networks<\/a><\/p>\n Francesco Orabona – Coin Betting for Backprop without Learning rates and More<\/a><\/p>\n Regina Barzilay – How Can NLP Help Cure Cancer?<\/a><\/p>\n Aapo Hyvarinen – Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning\u00a0<\/a><\/p>\n Max Welling – Generalizing Convolutions for Deep Learning <\/a><\/p>\n Opens in a new tab<\/span><\/p>\n","tab-content":[{"id":0,"name":"About","content":"Frontiers in Artificial Intelligence<\/span> is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial intelligence. Students, scientists, and engineers in academia and industry are all welcome to join us for these exciting talks and the opportunity to socialize with the Cambridge AI\/ML community.\r\n Tal Ben-Nun - Neural Code Comprehension: A Learnable Representation of Code Semantics<\/a><\/p>\r\n\r\n Finale Doshi-Velez \u2013 Interpretability in Machine Learning: What it means, How we\u2019re getting there.<\/a><\/p>\r\n\r\n<\/div>\r\n<\/div>\r\nFrancis Bach - Optimal algorithms for smooth and strongly convex distributed optimization in networks<\/a>\r\n\r\nFrancesco Orabona - Coin Betting for Backprop without Learning rates and More<\/a>\r\n\r\nRegina Barzilay - How Can NLP Help Cure Cancer?<\/a>\r\n\r\nAapo Hyvarinen - Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning\u00a0<\/a>\r\n\r\nMax Welling - Generalizing Convolutions for Deep Learning <\/a>\r\n\r\n "}],"msr_startdate":"2019-07-31","msr_enddate":"2019-07-31","msr_event_time":"11:00-12:00","msr_location":"MSR Cambridge","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"July 31, 2019","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"Frontiers in Artificial Intelligence is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial intelligence. Students, scientists, and engineers in academia and industry are all welcome to join us for these exciting talks and the opportunity to socialize with the Cambridge AI\/ML community. How good is your classifier? Revisiting the role of evaluation metrics…","msr_research_lab":[199561],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/600501","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/600501\/revisions"}],"predecessor-version":[{"id":1147034,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/600501\/revisions\/1147034"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=600501"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=600501"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=600501"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=600501"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=600501"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=600501"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=600501"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=600501"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=600501"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}How good is your classifier? Revisiting the role of evaluation metrics in machine learning<\/span><\/h3>\n
Sanmi Koyejo – University of Illinois<\/h4>\n
\nCambridge
\nCB1 2FB<\/p>\nHow good is your classifier? Revisiting the role of evaluation metrics in machine learning<\/span><\/h3>\n
Sanmi Koyejo – University of Illinois<\/h4>\n
How good is your classifier? Revisiting the role of evaluation metrics in machine learning<\/span><\/h3>\r\n
Sanmi Koyejo - University of Illinois<\/h4>\r\nWith the increasing integration of machine learning into real systems, it is crucial that trained models are optimized to reflect real-world tradeoffs. Increasing interest in proper evaluation has led to a wide variety of metrics employed in practice, often specially designed by experts. However, modern training strategies have not kept up with the explosion of metrics, leaving practitioners to resort to heuristics. To address this shortcoming, I will present a simple, yet consistent post-processing rule which improves the performance of trained binary, multilabel, and multioutput classifiers. Building on these results, I will propose a framework for metric elicitation, which addresses the broader question of how one might select an evaluation metric for real world problems so that it reflects true preferences."},{"id":1,"name":"Past Speakers","content":"