{"id":367799,"date":"2017-03-03T15:14:24","date_gmt":"2017-03-03T23:14:24","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=367799"},"modified":"2025-08-06T11:58:01","modified_gmt":"2025-08-06T18:58:01","slug":"new-england-machine-learning-day-2017","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2017\/","title":{"rendered":"New England Machine Learning Day 2017"},"content":{"rendered":"\n\n

Venue:<\/strong><\/p>\n

Microsoft Research New England<\/a>
\nHorace Mann Conference Room
\nOne Memorial Drive
\nCambridge, MA 02142<\/p>\n

Registration: <\/strong>Registration is now closed. Thank you for your interest in this year’s Machine Learning and we hope to see you next year!Opens in a new tab<\/span><\/p>\n

The sixth annual New England Machine Learning Day will be Friday, May 12, 2017, at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02142. The event will bring together local academics and researchers in Machine Learning, Artificial Intelligence, and their applications. There will be a lively poster session during lunch.<\/p>\n

Interested in helping improve fairness and reduce bias\/discrimination in ML? Attend\u00a0New England Machine Learning Hackathon: Hacking Bias in ML (opens in new tab)<\/span><\/a>,\u00a0the day before, Thursday May 11, at the same location.<\/p>\n

For talk abstracts, see the Agenda tab above.<\/p>\n

Schedule<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/strong><\/th>\nSession<\/strong><\/th>\n<\/tr>\n<\/thead>\n
\n
9:55\u201310:00<\/div>\n<\/td>\n
\n
Opening remarks<\/div>\n<\/td>\n<\/tr>\n
\n
10:00\u201310:30<\/div>\n<\/td>\n
\n
\n

Leslie Pack Kaelbling (opens in new tab)<\/span><\/a>,\u00a0Massachusetts Institute of Technology
\nIntelligent robots redux<\/p>\n<\/div>\n<\/td>\n<\/tr>\n

10:35\u201311:05<\/td>\nAlexander Rush (opens in new tab)<\/span><\/a>,\u00a0Harvard University
\nStructured attention networks<\/td>\n<\/tr>\n
11:10\u201311:40<\/td>\nLester Mackey<\/a>, Microsoft Research
\nMeasuring sample quality with Stein’s method<\/td>\n<\/tr>\n
11:40\u20131:45<\/td>\nLunch and posters<\/td>\n<\/tr>\n
1:45\u20132:15<\/td>\nThomas Serre (opens in new tab)<\/span><\/a>, Brown University
\nWhat are the visual features underlying human versus machine vision?<\/td>\n<\/tr>\n
2:20\u20132:50<\/td>\nDavid Sontag (opens in new tab)<\/span><\/a>,\u00a0Massachusetts Institute of Technology
\nCausal inference via deep learning<\/td>\n<\/tr>\n
2:50\u20133:20<\/td>\nCoffee break<\/td>\n<\/tr>\n
3:20\u20133:50<\/td>\nRoni Khardon (opens in new tab)<\/span><\/a>, Tufts University
\nEffective variational inference in non-conjugate 2-level latent variable models<\/td>\n<\/tr>\n
3:55\u20134:25<\/td>\nTina Eliassi-Rad (opens in new tab)<\/span><\/a>, Northeastern University
\nLearning, mining and graphs<\/td>\n<\/tr>\n
4:30\u20135:00<\/td>\nErik Learned-Miller (opens in new tab)<\/span><\/a>, University of Massachusetts Amherst
\nBootstrapping intelligence with motion estimation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Organizers<\/h2>\n