{"id":238827,"date":"2016-06-14T13:33:15","date_gmt":"2016-06-14T20:33:15","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&#038;p=238827"},"modified":"2022-08-08T10:14:48","modified_gmt":"2022-08-08T17:14:48","slug":"icml","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/icml\/","title":{"rendered":"International Conference on Machine Learning (ICML) 2016"},"content":{"rendered":"\n\n\n\n\n<p>ICML is the leading international machine learning conference and is supported by the <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.machinelearning.org\/\" target=\"_blank\" rel=\"noopener noreferrer\">International Machine Learning Society (IMLS)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>. The 33rd <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/icml.cc\/2016\/\" target=\"_blank\" rel=\"noopener noreferrer\">International Conference on Machine Learning (ICML 2016) <span class=\"sr-only\"> (opens in new tab)<\/span><\/a>was held in New York City, NY, USA, on June 19\u201324, 2016. ICML 2016 was colocated with <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.learningtheory.org\/colt2016\/\" target=\"_blank\" rel=\"noopener noreferrer\">COLT<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (June 24\u201326) and <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.auai.org\/uai2016\/\" target=\"_blank\" rel=\"noopener noreferrer\">UAI<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (June 24\u201329).<\/p>\n\n\n\n<p>Microsoft was proud to be a Gold Sponsor and as such had over 30 researchers attend and present. If you attended ICML 2016, we hope you stopped by our booth to chat with our researchers about the projects and opportunities at Microsoft that involve solving interesting machine and deep learning problems for billions of people. You can learn more about our research presented at ICML 2016 on the Workshops and Accepted Papers tabs, as well as on our blog:&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/microsoft-researchers-present-18-papers-at-icml\/\" target=\"_blank\" rel=\"noopener\">Microsoft researchers present 18 papers at the International Conference on Machine Learning ><\/a><\/p>\n\n\n\n<h2 id=\"committee-chairs\">Committee chairs<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jcl\/\">John Langford<\/a>, General Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/schapire\/\">Robert Schapire<\/a>, Financial Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/alekha\/\">Alekh Agarwal<\/a>, Area Chair<\/li><li>Sebastien Bubeck, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/adum\/\">Adam Kalai<\/a>, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/lihongli\/\">Lihong Li<\/a>, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/manik\/\">Manik Varma<\/a>, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/wallach\/\">Hanna Wallach<\/a>, Area Chair<\/li><\/ul>\n\n\n\n\n\n<h2 id=\"multi-view-representation-learning-mvrl\">Multi-View Representation Learning (MVRL)<\/h2>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xiaohe\/\">Xiaodong He<\/a> (Microsoft Research), Karen Livescu (TTI-Chicago), Weiran Wang (TTI-Chicago), Scott Wen-tau Yih (Microsoft Research)<\/p>\n\n\n\n<p>The <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/ttic.uchicago.edu\/~wwang5\/ICML2016_MVRL\/\" target=\"_blank\" rel=\"noopener noreferrer\">Multi-View Representation Learning (MVRL)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> workshop brought together researchers and practitioners in this area, and covered both theoretical and practical aspects of representation\/feature learning in the presence of multi-view data.<\/p>\n\n\n\n\n\n<ul class=\"wp-block-list\"><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">No Oops, You Won\u2019t Do It Again: Mechanisms for Self-Correction in Crowdsourcing<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Nihar Shah, UC Berkeley;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/denzho\/\">Dengyong Zhou<\/a>, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Dropout Distillation<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Samuel Rota Bul\u00f2, FBK;&nbsp;Lorenzo Porzi, FBK;&nbsp;Peter Kontschieder, Microsoft Research Cambridge<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Nathan Dowlin, Princeton;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/rang\/\">Ran Gilad-Bachrach<\/a>, Microsoft Research;&nbsp;Kim Laine, Microsoft Research;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/klauter\/\">Kristin Lauter<\/a>, Microsoft Research;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/mnaehrig\/\">Michael Naehrig<\/a>, Microsoft Research;&nbsp;John Wernsing, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Parameter Estimation for Generalized Thurstone Choice Models<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Milan Vojnovic, Microsoft; Seyoung Yun, Microsoft<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Network Morphism<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Tao Wei, University at Buffalo; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/chw\/\">Changhu Wang<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yongrui\/\">Yong Rui<\/a>, Microsoft Research; Chang Wen Chen<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Exact Exponent in Optimal Rates for Crowdsourcing<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Chao Gao, Yale University; Yu Lu, Yale University; Dengyong Zhou, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Doubly Robust Off-Policy Value Evaluation for Reinforcement Learning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Nan Jiang, University of Michigan; Lihong Li, Microsoft<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Analysis of Deep Neural Networks with Extended Data Jacobian Matrix<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Shengjie Wang, University of Washington; Abdel-rahman Mohamed, Rich Caruana, Microsoft; Jeff Bilmes, University of Washington; Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, UC Irvine; Ozlem Aslan<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/davidwip\/\">David Wipf<\/a>, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Non-Negative Matrix Factorization Under Heavy Noise<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Jagdeep Pani, Indian Institute of Science; Ravindran Kannan, Microsoft Research India; Chiranjib Bhattacharya; Navin Goyal, Microsoft Research India<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Optimal Classification with Multivariate Losses<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Nagarajan Natarajan, Microsoft Research India; Oluwasanmi Koyejo, Stanford University and University of Illinois at Urbana Champaign; Pradeep Ravikumar, UT Austin; Inderjit<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Efficient Algorithms for Adversarial Contextual Learning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/vasy\/\">Vasilis Syrgkanis<\/a>, Microsoft Research; Akshay Krishnamurthy, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/schapire\/\">Robert Schapire<\/a>, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Principal Component Projection Without Principal Component Analysis<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Roy Frostig, Stanford University; Cameron Musco, Massachusetts Institute of Technology; Christopher Musco, Mass. Institute of Technology; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Faster Eigenvector Computation via Shift-and-Invert Preconditioning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Dan Garber, TTI Chicago; Elad Hazan, Princeton University; Chi Jin, UC Berkeley; Sham, Cameron Musco, Massachusetts Institute of Technology; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/praneeth\/\">Praneeth Netrapalli<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Efficient Algorithms for Large-Scale Generalized Eigenvector Computation and CCA<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Rong Ge, Chi Jin, UC Berkeley;&nbsp;Sham, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/praneeth\/\">Praneeth Netrapalli<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">The Label Complexity of Mixed-Initiative Classifier Training<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Jina Suh, Microsoft;&nbsp;Xiaojin Zhu, University of Wisconsin;&nbsp;Saleema Amershi, Microsoft<\/li><li>\u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d by&nbsp;Aaron Schein, Mingyuan Zhou, Blei David, Columbia;&nbsp;Hanna Wallach, Microsoft<\/li><\/ul>\n\n\n","protected":false},"excerpt":{"rendered":"<p>ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS). The 33rd International Conference on Machine Learning (ICML 2016) was held in New York City on June 19\u201324, 2016. <\/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":"2016-06-19","msr_enddate":"2016-06-24","msr_location":"New York City, NY, USA","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[197900],"msr-event-type":[197941],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-238827","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-region-north-america","msr-event-type-conferences","msr-locale-en_us"],"msr_about":"<!-- wp:msr\/event-details {\"title\":\"International Conference on Machine Learning (ICML) 2016\"} \/-->\n\n<!-- wp:msr\/content-tabs -->\n<!-- wp:msr\/content-tab {\"title\":\"About\"} -->\n<!-- wp:paragraph -->\n<p>ICML is the leading international machine learning conference and is supported by the <a href=\"http:\/\/www.machinelearning.org\/\" target=\"_blank\" rel=\"noopener\">International Machine Learning Society (IMLS)<\/a>. The 33rd <a href=\"http:\/\/icml.cc\/2016\/\" target=\"_blank\" rel=\"noopener\">International Conference on Machine Learning (ICML 2016) <\/a>was held in New York City, NY, USA, on June 19\u201324, 2016. ICML 2016 was colocated with <a href=\"http:\/\/www.learningtheory.org\/colt2016\/\" target=\"_blank\" rel=\"noopener\">COLT<\/a> (June 24\u201326) and <a href=\"http:\/\/www.auai.org\/uai2016\/\" target=\"_blank\" rel=\"noopener\">UAI<\/a> (June 24\u201329).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Microsoft was proud to be a Gold Sponsor and as such had over 30 researchers attend and present. If you attended ICML 2016, we hope you stopped by our booth to chat with our researchers about the projects and opportunities at Microsoft that involve solving interesting machine and deep learning problems for billions of people. You can learn more about our research presented at ICML 2016 on the Workshops and Accepted Papers tabs, as well as on our blog:&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/microsoft-researchers-present-18-papers-at-icml\/\" target=\"_blank\" rel=\"noopener\">Microsoft researchers present 18 papers at the International Conference on Machine Learning &gt;<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:heading -->\n<h2>Committee chairs<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:list -->\n<ul><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jcl\/\">John Langford<\/a>, General Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/schapire\/\">Robert Schapire<\/a>, Financial Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/alekha\/\">Alekh Agarwal<\/a>, Area Chair<\/li><li>Sebastien Bubeck, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/adum\/\">Adam Kalai<\/a>, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/lihongli\/\">Lihong Li<\/a>, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/manik\/\">Manik Varma<\/a>, Area Chair<\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/wallach\/\">Hanna Wallach<\/a>, Area Chair<\/li><\/ul>\n<!-- \/wp:list -->\n<!-- \/wp:msr\/content-tab -->\n\n<!-- wp:msr\/content-tab {\"title\":\"Workshops\"} -->\n<!-- wp:heading -->\n<h2>Multi-View Representation Learning (MVRL)<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:paragraph -->\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xiaohe\/\">Xiaodong He<\/a> (Microsoft Research), Karen Livescu (TTI-Chicago), Weiran Wang (TTI-Chicago), Scott Wen-tau Yih (Microsoft Research)<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>The <a href=\"http:\/\/ttic.uchicago.edu\/~wwang5\/ICML2016_MVRL\/\" target=\"_blank\" rel=\"noopener\">Multi-View Representation Learning (MVRL)<\/a> workshop brought together researchers and practitioners in this area, and covered both theoretical and practical aspects of representation\/feature learning in the presence of multi-view data.<\/p>\n<!-- \/wp:paragraph -->\n<!-- \/wp:msr\/content-tab -->\n\n<!-- wp:msr\/content-tab {\"title\":\"Accepted Papers\"} -->\n<!-- wp:list -->\n<ul><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">No Oops, You Won\u2019t Do It Again: Mechanisms for Self-Correction in Crowdsourcing<\/a>\u201d by&nbsp;Nihar Shah, UC Berkeley;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/denzho\/\">Dengyong Zhou<\/a>, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Dropout Distillation<\/a>\u201d by&nbsp;Samuel Rota Bul\u00f2, FBK;&nbsp;Lorenzo Porzi, FBK;&nbsp;Peter Kontschieder, Microsoft Research Cambridge<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy<\/a>\u201d by&nbsp;Nathan Dowlin, Princeton;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/rang\/\">Ran Gilad-Bachrach<\/a>, Microsoft Research;&nbsp;Kim Laine, Microsoft Research;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/klauter\/\">Kristin Lauter<\/a>, Microsoft Research;&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/mnaehrig\/\">Michael Naehrig<\/a>, Microsoft Research;&nbsp;John Wernsing, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Parameter Estimation for Generalized Thurstone Choice Models<\/a>\u201d by&nbsp;Milan Vojnovic, Microsoft; Seyoung Yun, Microsoft<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Network Morphism<\/a>\u201d by&nbsp;Tao Wei, University at Buffalo; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/chw\/\">Changhu Wang<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yongrui\/\">Yong Rui<\/a>, Microsoft Research; Chang Wen Chen<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Exact Exponent in Optimal Rates for Crowdsourcing<\/a>\u201d by&nbsp;Chao Gao, Yale University; Yu Lu, Yale University; Dengyong Zhou, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Doubly Robust Off-Policy Value Evaluation for Reinforcement Learning<\/a>\u201d by&nbsp;Nan Jiang, University of Michigan; Lihong Li, Microsoft<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Analysis of Deep Neural Networks with Extended Data Jacobian Matrix<\/a>\u201d by&nbsp;Shengjie Wang, University of Washington; Abdel-rahman Mohamed, Rich Caruana, Microsoft; Jeff Bilmes, University of Washington; Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, UC Irvine; Ozlem Aslan<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation<\/a>\u201d by&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/davidwip\/\">David Wipf<\/a>, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Non-Negative Matrix Factorization Under Heavy Noise<\/a>\u201d by&nbsp;Jagdeep Pani, Indian Institute of Science; Ravindran Kannan, Microsoft Research India; Chiranjib Bhattacharya; Navin Goyal, Microsoft Research India<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Optimal Classification with Multivariate Losses<\/a>\u201d by&nbsp;Nagarajan Natarajan, Microsoft Research India; Oluwasanmi Koyejo, Stanford University and University of Illinois at Urbana Champaign; Pradeep Ravikumar, UT Austin; Inderjit<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Efficient Algorithms for Adversarial Contextual Learning<\/a>\u201d by&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/vasy\/\">Vasilis Syrgkanis<\/a>, Microsoft Research; Akshay Krishnamurthy, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/schapire\/\">Robert Schapire<\/a>, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Principal Component Projection Without Principal Component Analysis<\/a>\u201d by&nbsp;Roy Frostig, Stanford University; Cameron Musco, Massachusetts Institute of Technology; Christopher Musco, Mass. Institute of Technology; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Faster Eigenvector Computation via Shift-and-Invert Preconditioning<\/a>\u201d by&nbsp;Dan Garber, TTI Chicago; Elad Hazan, Princeton University; Chi Jin, UC Berkeley; Sham, Cameron Musco, Massachusetts Institute of Technology; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/praneeth\/\">Praneeth Netrapalli<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Efficient Algorithms for Large-Scale Generalized Eigenvector Computation and CCA<\/a>\u201d by&nbsp;Rong Ge, Chi Jin, UC Berkeley;&nbsp;Sham, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/praneeth\/\">Praneeth Netrapalli<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">The Label Complexity of Mixed-Initiative Classifier Training<\/a>\u201d by&nbsp;Jina Suh, Microsoft;&nbsp;Xiaojin Zhu, University of Wisconsin;&nbsp;Saleema Amershi, Microsoft<\/li><li>\u201c<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations<\/a>\u201d by&nbsp;Aaron Schein, Mingyuan Zhou, Blei David, Columbia;&nbsp;Hanna Wallach, Microsoft<\/li><\/ul>\n<!-- \/wp:list -->\n<!-- \/wp:msr\/content-tab -->\n<!-- \/wp:msr\/content-tabs -->","tab-content":[{"id":0,"name":"About","content":"ICML is the leading international machine learning conference and is supported by the <a href=\"http:\/\/www.machinelearning.org\/\" target=\"_blank\">International Machine Learning Society (IMLS)<\/a>. The 33rd <a href=\"http:\/\/icml.cc\/2016\/\" target=\"_blank\">International Conference on Machine Learning (ICML 2016) <\/a>was held in New York City, NY, USA, on June 19\u201324, 2016. ICML 2016 was colocated with <a href=\"http:\/\/www.learningtheory.org\/colt2016\/\" target=\"_blank\">COLT<\/a> (June 24\u201326) and <a href=\"http:\/\/www.auai.org\/uai2016\/\" target=\"_blank\">UAI<\/a> (June 24\u201329).\r\n\r\nMicrosoft was proud to be a Gold Sponsor and as such had over 30 researchers attend and present. If you attended ICML 2016, we hope you stopped by our booth to chat with our researchers about the projects and opportunities at Microsoft that involve solving interesting machine and deep learning problems for billions of people. You can learn more about our research presented at ICML 2016 on the Workshops and Accepted Papers tabs, as well as on our blog:\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/microsoft-researchers-present-18-papers-at-icml\/\" target=\"_blank\">Microsoft researchers present 18 papers at the International Conference on Machine Learning &gt;<\/a>\r\n<h2>Committee chairs<\/h2>\r\n<ul>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jcl\/\">John Langford<\/a>, General Chair<\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/schapire\/\">Robert Schapire<\/a>, Financial Chair<\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/alekha\/\">Alekh Agarwal<\/a>, Area Chair<\/li>\r\n \t<li>Sebastien Bubeck, Area Chair<\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/adum\/\">Adam Kalai<\/a>, Area Chair<\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/lihongli\/\">Lihong Li<\/a>, Area Chair<\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/manik\/\">Manik Varma<\/a>, Area Chair<\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/wallach\/\">Hanna Wallach<\/a>, Area Chair<\/li>\r\n<\/ul>"},{"id":1,"name":"Workshops","content":"<h2>Multi-View Representation Learning (MVRL)<\/h2>\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xiaohe\/\">Xiaodong He<\/a> (Microsoft Research), Karen Livescu (TTI-Chicago), Weiran Wang (TTI-Chicago), Scott Wen-tau Yih (Microsoft Research)\r\n\r\nThe <a href=\"http:\/\/ttic.uchicago.edu\/~wwang5\/ICML2016_MVRL\/\" target=\"_blank\">Multi-View Representation Learning (MVRL)<\/a> workshop brought together researchers and practitioners in this area, and covered both theoretical and practical aspects of representation\/feature learning in the presence of multi-view data."},{"id":2,"name":"Accepted Papers","content":"<ul>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">No Oops, You Won\u2019t Do It Again: Mechanisms for Self-Correction in Crowdsourcing<\/a>\" by\u00a0Nihar Shah, UC Berkeley;\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/denzho\/\">Dengyong Zhou<\/a>, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Dropout Distillation<\/a>\" by\u00a0Samuel Rota Bul\u00f2, FBK;\u00a0Lorenzo Porzi, FBK;\u00a0Peter Kontschieder, Microsoft Research Cambridge<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy<\/a>\" by\u00a0Nathan Dowlin, Princeton;\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/rang\/\">Ran Gilad-Bachrach<\/a>, Microsoft Research;\u00a0Kim Laine, Microsoft Research;\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/klauter\/\">Kristin Lauter<\/a>, Microsoft Research;\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/mnaehrig\/\">Michael Naehrig<\/a>, Microsoft Research;\u00a0John Wernsing, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Parameter Estimation for Generalized Thurstone Choice Models<\/a>\" by\u00a0Milan Vojnovic, Microsoft; Seyoung Yun, Microsoft<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Network Morphism<\/a>\" by\u00a0Tao Wei, University at Buffalo; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/chw\/\">Changhu Wang<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yongrui\/\">Yong Rui<\/a>, Microsoft Research; Chang Wen Chen<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Exact Exponent in Optimal Rates for Crowdsourcing<\/a>\" by\u00a0Chao Gao, Yale University; Yu Lu, Yale University; Dengyong Zhou, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Doubly Robust Off-Policy Value Evaluation for Reinforcement Learning<\/a>\" by\u00a0Nan Jiang, University of Michigan; Lihong Li, Microsoft<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Analysis of Deep Neural Networks with Extended Data Jacobian Matrix<\/a>\" by\u00a0Shengjie Wang, University of Washington; Abdel-rahman Mohamed, Rich Caruana, Microsoft; Jeff Bilmes, University of Washington; Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, UC Irvine; Ozlem Aslan<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation<\/a>\" by\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/davidwip\/\">David Wipf<\/a>, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Non-Negative Matrix Factorization Under Heavy Noise<\/a>\" by\u00a0Jagdeep Pani, Indian Institute of Science; Ravindran Kannan, Microsoft Research India; Chiranjib Bhattacharya; Navin Goyal, Microsoft Research India<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Optimal Classification with Multivariate Losses<\/a>\" by\u00a0Nagarajan Natarajan, Microsoft Research India; Oluwasanmi Koyejo, Stanford University and University of Illinois at Urbana Champaign; Pradeep Ravikumar, UT Austin; Inderjit<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Efficient Algorithms for Adversarial Contextual Learning<\/a>\" by\u00a0<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/vasy\/\">Vasilis Syrgkanis<\/a>, Microsoft Research; Akshay Krishnamurthy, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/schapire\/\">Robert Schapire<\/a>, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Principal Component Projection Without Principal Component Analysis<\/a>\" by\u00a0Roy Frostig, Stanford University; Cameron Musco, Massachusetts Institute of Technology; Christopher Musco, Mass. Institute of Technology; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Faster Eigenvector Computation via Shift-and-Invert Preconditioning<\/a>\" by\u00a0Dan Garber, TTI Chicago; Elad Hazan, Princeton University; Chi Jin, UC Berkeley; Sham, Cameron Musco, Massachusetts Institute of Technology; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/praneeth\/\">Praneeth Netrapalli<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Efficient Algorithms for Large-Scale Generalized Eigenvector Computation and CCA<\/a>\" by\u00a0Rong Ge, Chi Jin, UC Berkeley;\u00a0Sham, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/praneeth\/\">Praneeth Netrapalli<\/a>, Microsoft Research; <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/asid\/\">Aaron Sidford<\/a>, Microsoft Research<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">The Label Complexity of Mixed-Initiative Classifier Training<\/a>\" by\u00a0Jina Suh, Microsoft;\u00a0Xiaojin Zhu, University of Wisconsin;\u00a0Saleema Amershi, Microsoft<\/li>\r\n \t<li>\"<a href=\"http:\/\/icml.cc\/2016\/?page_id=1649\">Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations<\/a>\" by\u00a0Aaron Schein, Mingyuan Zhou, Blei David, Columbia;\u00a0Hanna Wallach, Microsoft<\/li>\r\n<\/ul>"}],"msr_startdate":"2016-06-19","msr_enddate":"2016-06-24","msr_event_time":"","msr_location":"New York City, NY, USA","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"June 19, 2016","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS). The 33rd International Conference on Machine Learning (ICML 2016) was held in New York City on June 19\u201324, 2016.","msr_research_lab":[],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[823105],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/238827","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":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/238827\/revisions"}],"predecessor-version":[{"id":868029,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/238827\/revisions\/868029"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=238827"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=238827"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=238827"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=238827"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=238827"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=238827"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=238827"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=238827"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=238827"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}