{"id":283373,"date":"2016-08-25T12:20:26","date_gmt":"2016-08-25T19:20:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&#038;p=283373"},"modified":"2025-08-06T11:59:33","modified_gmt":"2025-08-06T18:59:33","slug":"new-england-machine-learning-day-2012","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2012\/","title":{"rendered":"New England Machine Learning Day 2012"},"content":{"rendered":"\n\n<p><strong>Venue:<\/strong> <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-new-england\/\" target=\"_blank\" rel=\"noopener noreferrer\">Microsoft Research New England<\/a><br \/>\nHorace Mann Conference Room<br \/>\nOne Memorial Drive<br \/>\nCambridge, MA 02142<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>The first New England Machine Learning Day will be held May 16, 2012, from 9:50 AM to 5:40 PM at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02138. The event will bring together local academics and researchers in machine learning and its applications. There will be a lively poster session during lunch.\u00a0See the agenda tab for the list of presentations.<\/p>\n<h2>Related events<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2020\/\">NEML 2020<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2019\/\">NEML 2019<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2018\/\">NEML 2018<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2017\/\">NEML 2017<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2016\/\">NEML 2016<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2015\/\">NEML 2015<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2014\/\">NEML 2014<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2013\/\">NEML 2013<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2012\/\">NEML 2012<\/a><\/li>\n<\/ul>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>9:50-10:00<br \/>\nOpening remarks<\/p>\n<p>10:00-10:40,<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.csail.mit.edu\/tommi\/\"> Tommi Jaakkola<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (MIT)<br \/>\n<em>Scaling structured prediction<\/em><\/p>\n<p>10:45-11:25, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.cs.umass.edu\/~mccallum\/\">Andrew McCallum<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (UMass Amherst)<br \/>\n<em>Joint inference and probabilistic databases for large-scale knowledge-base construction<\/em><\/p>\n<p>Wikipedia\u2019s impact has been revolutionary. The collaboratively edited encyclopedia has transformed the way many people learn, browse new interests, share knowledge and make decisions. Its information is mainly represented in natural language text. However, for many tasks more structured information is useful because it better supports pattern analysis and decision-making. In this talk I will describe multiple research components useful for building large, structured knowledge bases, including information extraction from text, entity resolution, joint inference with conditional random fields, probabilistic databases to manage uncertainty at scale, robust reasoning about human edits, tight integration of probabilistic inference and parallel\/distributed processing, and probabilistic programming languages for easy specification of complex graphical models. I will also discuss applications of these methods to scientometrics and a new publishing model for science research. Joint work with Michael Wick, Sameer Singh, Karl Schultz, Sebastian Riedel, Limin Yao, Brian Martin and Gerome Miklau.<\/p>\n<p>11:30-12:10, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.seas.harvard.edu\/~valiant\/\">Leslie Valiant<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Harvard)<br \/>\n<em>Reasoning on learned knowledge<\/em><\/p>\n<p>12:10-1:40<br \/>\nLunch and posters<\/p>\n<p>1:45-2:25, Ohad Shamir (MSR)<br \/>\n<em>New approaches to collaborative filtering<\/em><\/p>\n<p>Collaborative Filtering (CF) is a common approach for recommender systems, which utilize information from many users&#8217; prior preferences. In particular, a matrix-based approach, which attempts to predict by fitting a low-rank or low-norm matrix to the observed data, has proven highly effective in modern CF tasks, such as the Netflix challenge. In this talk, I will outline two recent contributions in this direction. The first is a simple and principled algorithm to find a low-rank solution to large-scale convex optimization problems, such as those encountered in CF. The second is the development of online learning algorithms for CF, which can potentially provide very strong guarantees under minimal assumptions on the users&#8217; behavior. Moreover, the unique nature of CF hinders the application of standard online learning techniques, and requires some fundamentally new approaches which might be of independent interest. Based on joint works with Nicol\u00f2 Cesa-Bianchi, Alon Gonen, Sasha Rakhlin, Shai Shalev-Shwartz and Karthik Sridharan.<\/p>\n<p>2:30-3:10, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/cs.brown.edu\/~pff\/\">Pedro Felzenszwalb<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Brown)<br \/>\n<em>Object detection with grammar models<\/em><\/p>\n<p>3:10-3:30<br \/>\nCoffee Break<\/p>\n<p>3:30-4:10, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.people.fas.harvard.edu\/~airoldi\/\">Edo Airoldi<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Harvard)<br \/>\n<em>Valid inference in high-throughput biology and social media marketing<\/em><\/p>\n<p>4:15-4:55, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.csail.mit.edu\/regina\/\">Regina Barzilay<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (MIT)<br \/>\n<em>Multilingual learning via selective sharing<\/em><\/p>\n<p>5:00-5:40, Ce Liu (MSR)<br \/>\n<em>A dense correspondence framework for visual computing<\/em><\/p>\n<p><em>What has been will be again, what has been done will be done again; there is nothing new under the sun &#8212; Ecclesiastes 1:9. <\/em><\/p>\n<p>While we walk on a street, we stare at a 3D scene and see the same objects from different views. In our lives, we look at our home, workplaces, colleagues, family members and friends every day. Even when we travel to different countries, this world consists of similar objects and scenes that we have seen before. Therefore, repetitiveness (also called sparsity) is an important characteristics of images and videos for visual computing.<\/p>\n<p>Repetition of visual patterns across frames is established by means of correspondences, as pixels and objects move from frame to frame. In this talk, we will exploit dense correspondences for visual computing. We start with videos, where dense correspondences can be naturally formulated as motion. We show how accurate motion estimation can be used for video reconstruction (denoising, super resolution, deblocking), and discuss possible directions for long-range motion representation. We will move to a large set of images, where dense correspondences can be captured by SIFT flow, an algorithm that is able to align images across different scenes. Now that pixels and visual patterns are dense connected across images, we are able to treat a large collection of images just like videos, and perform both low-level image reconstruction and high-level image understanding on a large graph.<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>Sham Kakade, Microsoft Research, Chair<br \/>\nRyan Adams, Harvard<br \/>\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/adum\/\" target=\"_blank\" rel=\"noopener noreferrer\">Adam Tauman Kalai<\/a>, Microsoft Research<br \/>\nCynthia Rudin, MIT<br \/>\nJoshua Tenenbaum, MIT<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The first New England Machine Learning Day will be held May 16, 2012, from 9:50 AM to 5:40 PM at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02138. The event will bring together local academics and researchers in machine learning and its applications. There will be a lively poster session during lunch. <\/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":"2012-05-16","msr_enddate":"2012-05-16","msr_location":"Cambridge, MA, USA","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"9:50 AM\u20135:40 PM","msr_hide_region":false,"msr_private_event":true,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[197900],"msr-event-type":[197944],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-283373","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-region-north-america","msr-event-type-hosted-by-microsoft","msr-locale-en_us"],"msr_about":"<!-- wp:msr\/event-details {\"title\":\"New England Machine Learning Day 2012\",\"backgroundColor\":\"grey\",\"image\":{\"id\":381722,\"url\":\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/03\/MSR-MachineLearningDay-Hero_1920x720-v1.jpg\",\"alt\":\"\"}} \/-->\n\n<!-- wp:msr\/content-tabs --><!-- wp:msr\/content-tab {\"title\":\"About\"} --><!-- wp:freeform --><p><strong>Venue:<\/strong> <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-new-england\/\" target=\"_blank\" rel=\"noopener noreferrer\">Microsoft Research New England<\/a><br \/>\nHorace Mann Conference Room<br \/>\nOne Memorial Drive<br \/>\nCambridge, MA 02142<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>The first New England Machine Learning Day will be held May 16, 2012, from 9:50 AM to 5:40 PM at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02138. The event will bring together local academics and researchers in machine learning and its applications. There will be a lively poster session during lunch.\u00a0See the agenda tab for the list of presentations.<\/p>\n<h2>Related events<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2020\/\">NEML 2020<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2019\/\">NEML 2019<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2018\/\">NEML 2018<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2017\/\">NEML 2017<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2016\/\">NEML 2016<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2015\/\">NEML 2015<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2014\/\">NEML 2014<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2013\/\">NEML 2013<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2012\/\">NEML 2012<\/a><\/li>\n<\/ul>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Agenda\"} --><!-- wp:freeform --><p>9:50-10:00<br \/>\nOpening remarks<\/p>\n<p>10:00-10:40,<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.csail.mit.edu\/tommi\/\"> Tommi Jaakkola<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (MIT)<br \/>\n<em>Scaling structured prediction<\/em><\/p>\n<p>10:45-11:25, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.cs.umass.edu\/~mccallum\/\">Andrew McCallum<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (UMass Amherst)<br \/>\n<em>Joint inference and probabilistic databases for large-scale knowledge-base construction<\/em><\/p>\n<p>Wikipedia\u2019s impact has been revolutionary. The collaboratively edited encyclopedia has transformed the way many people learn, browse new interests, share knowledge and make decisions. Its information is mainly represented in natural language text. However, for many tasks more structured information is useful because it better supports pattern analysis and decision-making. In this talk I will describe multiple research components useful for building large, structured knowledge bases, including information extraction from text, entity resolution, joint inference with conditional random fields, probabilistic databases to manage uncertainty at scale, robust reasoning about human edits, tight integration of probabilistic inference and parallel\/distributed processing, and probabilistic programming languages for easy specification of complex graphical models. I will also discuss applications of these methods to scientometrics and a new publishing model for science research. Joint work with Michael Wick, Sameer Singh, Karl Schultz, Sebastian Riedel, Limin Yao, Brian Martin and Gerome Miklau.<\/p>\n<p>11:30-12:10, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.seas.harvard.edu\/~valiant\/\">Leslie Valiant<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Harvard)<br \/>\n<em>Reasoning on learned knowledge<\/em><\/p>\n<p>12:10-1:40<br \/>\nLunch and posters<\/p>\n<p>1:45-2:25, Ohad Shamir (MSR)<br \/>\n<em>New approaches to collaborative filtering<\/em><\/p>\n<p>Collaborative Filtering (CF) is a common approach for recommender systems, which utilize information from many users&#8217; prior preferences. In particular, a matrix-based approach, which attempts to predict by fitting a low-rank or low-norm matrix to the observed data, has proven highly effective in modern CF tasks, such as the Netflix challenge. In this talk, I will outline two recent contributions in this direction. The first is a simple and principled algorithm to find a low-rank solution to large-scale convex optimization problems, such as those encountered in CF. The second is the development of online learning algorithms for CF, which can potentially provide very strong guarantees under minimal assumptions on the users&#8217; behavior. Moreover, the unique nature of CF hinders the application of standard online learning techniques, and requires some fundamentally new approaches which might be of independent interest. Based on joint works with Nicol\u00f2 Cesa-Bianchi, Alon Gonen, Sasha Rakhlin, Shai Shalev-Shwartz and Karthik Sridharan.<\/p>\n<p>2:30-3:10, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/cs.brown.edu\/~pff\/\">Pedro Felzenszwalb<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Brown)<br \/>\n<em>Object detection with grammar models<\/em><\/p>\n<p>3:10-3:30<br \/>\nCoffee Break<\/p>\n<p>3:30-4:10, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/www.people.fas.harvard.edu\/~airoldi\/\">Edo Airoldi<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Harvard)<br \/>\n<em>Valid inference in high-throughput biology and social media marketing<\/em><\/p>\n<p>4:15-4:55, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"http:\/\/people.csail.mit.edu\/regina\/\">Regina Barzilay<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (MIT)<br \/>\n<em>Multilingual learning via selective sharing<\/em><\/p>\n<p>5:00-5:40, Ce Liu (MSR)<br \/>\n<em>A dense correspondence framework for visual computing<\/em><\/p>\n<p><em>What has been will be again, what has been done will be done again; there is nothing new under the sun &#8212; Ecclesiastes 1:9. <\/em><\/p>\n<p>While we walk on a street, we stare at a 3D scene and see the same objects from different views. In our lives, we look at our home, workplaces, colleagues, family members and friends every day. Even when we travel to different countries, this world consists of similar objects and scenes that we have seen before. Therefore, repetitiveness (also called sparsity) is an important characteristics of images and videos for visual computing.<\/p>\n<p>Repetition of visual patterns across frames is established by means of correspondences, as pixels and objects move from frame to frame. In this talk, we will exploit dense correspondences for visual computing. We start with videos, where dense correspondences can be naturally formulated as motion. We show how accurate motion estimation can be used for video reconstruction (denoising, super resolution, deblocking), and discuss possible directions for long-range motion representation. We will move to a large set of images, where dense correspondences can be captured by SIFT flow, an algorithm that is able to align images across different scenes. Now that pixels and visual patterns are dense connected across images, we are able to treat a large collection of images just like videos, and perform both low-level image reconstruction and high-level image understanding on a large graph.<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Committee\"} --><!-- wp:freeform --><p>Sham Kakade, Microsoft Research, Chair<br \/>\nRyan Adams, Harvard<br \/>\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/adum\/\" target=\"_blank\" rel=\"noopener noreferrer\">Adam Tauman Kalai<\/a>, Microsoft Research<br \/>\nCynthia Rudin, MIT<br \/>\nJoshua Tenenbaum, MIT<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- \/wp:msr\/content-tabs -->","tab-content":[{"id":0,"name":"About","content":"The first New England Machine Learning Day will be held May 16, 2012, from 9:50 AM to 5:40 PM at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02138. The event will bring together local academics and researchers in machine learning and its applications. There will be a lively poster session during lunch.\u00a0See the agenda tab for the list of presentations.\r\n<h2>Related events<\/h2>\r\n<ul>\r\n        <li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2020\/\">NEML 2020<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2019\/\">NEML 2019<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2018\/\">NEML 2018<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2017\/\">NEML 2017<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2016\/\">NEML 2016<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2015\/\">NEML 2015<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2014\/\">NEML 2014<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2013\/\">NEML 2013<\/a><\/li>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2012\/\">NEML 2012<\/a><\/li>\r\n<\/ul>"},{"id":1,"name":"Agenda","content":"9:50-10:00\r\nOpening remarks\r\n\r\n10:00-10:40,<a href=\"http:\/\/people.csail.mit.edu\/tommi\/\"> Tommi Jaakkola<\/a> (MIT)\r\n<em>Scaling structured prediction<\/em>\r\n\r\n10:45-11:25, <a href=\"http:\/\/people.cs.umass.edu\/~mccallum\/\">Andrew McCallum<\/a> (UMass Amherst)\r\n<em>Joint inference and probabilistic databases for large-scale knowledge-base construction<\/em>\r\n\r\nWikipedia\u2019s impact has been revolutionary. The collaboratively edited encyclopedia has transformed the way many people learn, browse new interests, share knowledge and make decisions. Its information is mainly represented in natural language text. However, for many tasks more structured information is useful because it better supports pattern analysis and decision-making. In this talk I will describe multiple research components useful for building large, structured knowledge bases, including information extraction from text, entity resolution, joint inference with conditional random fields, probabilistic databases to manage uncertainty at scale, robust reasoning about human edits, tight integration of probabilistic inference and parallel\/distributed processing, and probabilistic programming languages for easy specification of complex graphical models. I will also discuss applications of these methods to scientometrics and a new publishing model for science research. Joint work with Michael Wick, Sameer Singh, Karl Schultz, Sebastian Riedel, Limin Yao, Brian Martin and Gerome Miklau.\r\n\r\n11:30-12:10, <a href=\"http:\/\/people.seas.harvard.edu\/~valiant\/\">Leslie Valiant<\/a> (Harvard)\r\n<em>Reasoning on learned knowledge<\/em>\r\n\r\n12:10-1:40\r\nLunch and posters\r\n\r\n1:45-2:25, Ohad Shamir (MSR)\r\n<em>New approaches to collaborative filtering<\/em>\r\n\r\nCollaborative Filtering (CF) is a common approach for recommender systems, which utilize information from many users' prior preferences. In particular, a matrix-based approach, which attempts to predict by fitting a low-rank or low-norm matrix to the observed data, has proven highly effective in modern CF tasks, such as the Netflix challenge. In this talk, I will outline two recent contributions in this direction. The first is a simple and principled algorithm to find a low-rank solution to large-scale convex optimization problems, such as those encountered in CF. The second is the development of online learning algorithms for CF, which can potentially provide very strong guarantees under minimal assumptions on the users' behavior. Moreover, the unique nature of CF hinders the application of standard online learning techniques, and requires some fundamentally new approaches which might be of independent interest. Based on joint works with Nicol\u00f2 Cesa-Bianchi, Alon Gonen, Sasha Rakhlin, Shai Shalev-Shwartz and Karthik Sridharan.\r\n\r\n2:30-3:10, <a href=\"http:\/\/cs.brown.edu\/~pff\/\">Pedro Felzenszwalb<\/a> (Brown)\r\n<em>Object detection with grammar models<\/em>\r\n\r\n3:10-3:30\r\nCoffee Break\r\n\r\n3:30-4:10, <a href=\"http:\/\/www.people.fas.harvard.edu\/~airoldi\/\">Edo Airoldi<\/a> (Harvard)\r\n<em>Valid inference in high-throughput biology and social media marketing<\/em>\r\n\r\n4:15-4:55, <a href=\"http:\/\/people.csail.mit.edu\/regina\/\">Regina Barzilay<\/a> (MIT)\r\n<em>Multilingual learning via selective sharing<\/em>\r\n\r\n5:00-5:40, Ce Liu (MSR)\r\n<em>A dense correspondence framework for visual computing<\/em>\r\n\r\n<em>What has been will be again, what has been done will be done again; there is nothing new under the sun -- Ecclesiastes 1:9. <\/em>\r\n\r\nWhile we walk on a street, we stare at a 3D scene and see the same objects from different views. In our lives, we look at our home, workplaces, colleagues, family members and friends every day. Even when we travel to different countries, this world consists of similar objects and scenes that we have seen before. Therefore, repetitiveness (also called sparsity) is an important characteristics of images and videos for visual computing.\r\n\r\nRepetition of visual patterns across frames is established by means of correspondences, as pixels and objects move from frame to frame. In this talk, we will exploit dense correspondences for visual computing. We start with videos, where dense correspondences can be naturally formulated as motion. We show how accurate motion estimation can be used for video reconstruction (denoising, super resolution, deblocking), and discuss possible directions for long-range motion representation. We will move to a large set of images, where dense correspondences can be captured by SIFT flow, an algorithm that is able to align images across different scenes. Now that pixels and visual patterns are dense connected across images, we are able to treat a large collection of images just like videos, and perform both low-level image reconstruction and high-level image understanding on a large graph."},{"id":2,"name":"Committee","content":"Sham Kakade, Microsoft Research, Chair\r\nRyan Adams, Harvard\r\n<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/adum\/\" target=\"_blank\" rel=\"noopener noreferrer\">Adam Tauman Kalai<\/a>, Microsoft Research\r\nCynthia Rudin, MIT\r\nJoshua Tenenbaum, MIT"}],"msr_startdate":"2012-05-16","msr_enddate":"2012-05-16","msr_event_time":"9:50 AM\u20135:40 PM","msr_location":"Cambridge, MA, USA","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"May 16, 2012","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":"<img width=\"960\" height=\"360\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/03\/MSR-MachineLearningDay-Hero_1920x720-v1.jpg\" class=\"img-object-cover\" alt=\"Portrait on green background, header for New England Machine Learning Day event page\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/03\/MSR-MachineLearningDay-Hero_1920x720-v1.jpg 1920w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/03\/MSR-MachineLearningDay-Hero_1920x720-v1-300x113.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/03\/MSR-MachineLearningDay-Hero_1920x720-v1-768x288.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/03\/MSR-MachineLearningDay-Hero_1920x720-v1-1024x384.jpg 1024w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/>","event_excerpt":"The first New England Machine Learning Day will be held May 16, 2012, from 9:50 AM to 5:40 PM at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02138. The event will bring together local academics and researchers in machine learning and its applications. There will be a lively poster session during lunch.","msr_research_lab":[199563],"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\/283373","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":4,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/283373\/revisions"}],"predecessor-version":[{"id":1147281,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/283373\/revisions\/1147281"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/381722"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=283373"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=283373"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=283373"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=283373"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=283373"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=283373"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=283373"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=283373"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=283373"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}