{"id":559302,"date":"2019-01-08T02:20:26","date_gmt":"2019-01-08T10:20:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=559302"},"modified":"2020-01-20T13:02:46","modified_gmt":"2020-01-20T21:02:46","slug":"new-england-machine-learning-day-2019","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2019\/","title":{"rendered":"New England Machine Learning Day 2019"},"content":{"rendered":"

Venue:<\/strong> Northeastern University
\nInterdisciplinary Science & Engineering Complex, ISEC
\n805 Columbus Ave, Boston, MA 02120 (opens in new tab)<\/span><\/a><\/p>\n

Sponsor:<\/strong> Microsoft Research<\/p>\n

Registration<\/strong> is currently closed, but we have additional spots for poster presenters, so please do submit even if you have not yet registered!<\/p>\n","protected":false},"excerpt":{"rendered":"

The eighth annual New England Machine Learning Day will take place on May 10, 2019, at Northeastern University. The event will bring together local academics and researchers in Machine Learning, Artificial Intelligence, and their applications. Stay tuned for further details and save the date.<\/p>\n","protected":false},"featured_media":381722,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2019-05-10","msr_enddate":"2019-05-10","msr_location":"Boston, Massachusetts, 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,"footnotes":""},"research-area":[13556],"msr-region":[197900],"msr-event-type":[197947],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-559302","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-region-north-america","msr-event-type-universities","msr-locale-en_us"],"msr_about":"Venue:<\/strong> Northeastern University\r\nInterdisciplinary Science & Engineering Complex, ISEC\r\n805 Columbus Ave, Boston, MA 02120<\/a>\r\n\r\nSponsor:<\/strong> Microsoft Research\r\n\r\nRegistration<\/strong> is currently closed, but we have additional spots for poster presenters, so please do submit even if you have not yet registered!","tab-content":[{"id":0,"name":"About","content":"

Are you looking for NEML 2020<\/a>?<\/h2>\r\n\r\nThe eighth annual New England Machine Learning Day will take place on May 10, 2019<\/strong>, at Northeastern University. The event will bring together local academics and researchers in Machine Learning, Artificial Intelligence, and their applications. Open registration is currently closed, but we have additional spots for poster presenters, so please do submit even if you have not yet registered!\r\n

Schedule<\/h2>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Time<\/strong><\/th>\r\nSession<\/strong><\/th>\r\n<\/tr>\r\n<\/thead>\r\n
\r\n
9:55 \u2013 10:00<\/div><\/td>\r\n
\r\n
Opening remarks<\/div><\/td>\r\n<\/tr>\r\n
\r\n
10:00 \u2013 10:30<\/div><\/td>\r\n
\r\n
Caroline Uhler<\/a>, MIT\r\nAutoencoders: From Data Integration to Memorization<\/div><\/td>\r\n<\/tr>\r\n
10:30 \u2013 11:00<\/td>\r\n\r\n
Issa Dahabreh<\/a>, Brown\r\nTransporting inferences from an experiment to a new population when the covariates are high-dimensional<\/div><\/td>\r\n<\/tr>\r\n
11:00 \u2013 11:30<\/td>\r\nPosters<\/td>\r\n<\/tr>\r\n
11:30 \u2013 12:45<\/td>\r\nLunch (and more posters)<\/td>\r\n<\/tr>\r\n
12:45 \u2013 1:45<\/td>\r\nPanel: ML and Industry -- the good, the bad and the ugly:\r\n
1:50 \u2013 2:20<\/td>\r\nNicole Immorlica<\/a>, Microsoft Research\r\nThe Impact of Signaling on Fair Outcomes in Strategic Classification<\/span><\/td>\r\n<\/tr>\r\n
2:20 \u2013 2:50<\/td>\r\nFlavio du Pin Calmon<\/a>, Harvard\r\nOn representations and fairness: a few information-theoretic tools for machine learning<\/span><\/td>\r\n<\/tr>\r\n
2:50 \u2013 3:20<\/td>\r\nCoffee<\/td>\r\n<\/tr>\r\n
3:20 \u2013 3:50<\/td>\r\nJan-Willem van de Meent<\/a>, Northeastern\r\nIntegrating Deep Learning and Probabilistic Programming\r\n<\/span><\/td>\r\n<\/tr>\r\n
3:50 \u2013 4:20<\/td>\r\nEllie Pavlick<\/a>, Brown\r\nWhat should constitute natural language \"understanding\u201d?<\/td>\r\n<\/tr>\r\n
4:20 \u2013 4:50<\/td>\r\nFrancesco Orabona<\/a>, Boston University\r\nCoin Betting for Backprop without Learning Rates and More<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

Organizing committee<\/h2>\r\n