{"id":199960,"date":"2014-04-02T13:28:34","date_gmt":"2014-04-02T13:28:34","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/events\/new-england-machine-learning-day-2014\/"},"modified":"2020-01-20T13:05:27","modified_gmt":"2020-01-20T21:05:27","slug":"new-england-machine-learning-day-2014","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/new-england-machine-learning-day-2014\/","title":{"rendered":"New England Machine Learning Day 2014"},"content":{"rendered":"
Venue:<\/strong> Microsoft Research New England<\/a> The third annual New England Machine Learning Day will be held May 13, 2014, at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02142. The event will bring together local academics and researchers in machine learning and its applications.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"msr_startdate":"2014-05-13","msr_enddate":"2014-05-13","msr_location":"Cambridge, MA, 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":true,"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-199960","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-region-north-america","msr-event-type-hosted-by-microsoft","msr-locale-en_us"],"msr_about":"Venue:<\/strong> Microsoft Research New England<\/a>\r\nHorace Mann Conference Room\r\nOne Memorial Drive\r\nCambridge, MA 02142","tab-content":[{"id":0,"name":"About","content":"The third annual New England Machine Learning Day will be held May 13, 2014, at Microsoft Research New England, One Memorial Drive, Cambridge, MA 02142. The event will bring together local academics and researchers in machine learning and its applications. See the agenda tab for the list of presentations.\r\n
\nHorace Mann Conference Room
\nOne Memorial Drive
\nCambridge, MA 02142<\/p>\n","protected":false},"excerpt":{"rendered":"Related events<\/h2>\r\n
\r\n
\r\n\r\n
\r\n Poster Title\u00a0<\/strong><\/td>\r\n Presenting Author\/\r\nComplete List of Authors<\/strong><\/td>\r\n<\/tr>\r\n \r\n Inferring Multilateral Relations from Dynamic Bilateral Interactions<\/td>\r\n Aaron Schein \/ Aaron Schein, Juston Moore, Hanna Wallach<\/td>\r\n<\/tr>\r\n \r\n Sparse Neural Networks and Random-Access Pixel Cameras for Energy Efficient Mobile Gaze Tracking<\/td>\r\n Addison Mayberry \/ Addison Mayberry, Pan Hu, Christopher Salthouse, Benjamin Marlin, Deepak Ganesan<\/td>\r\n<\/tr>\r\n \r\n Relational Dependency Networks for Anomaly Detection<\/td>\r\n Amanda Gentzel \/ Amanda Gentzel, Elisabeth Baseman, Dan Corkill, David Jensen<\/td>\r\n<\/tr>\r\n \r\n Dynamically Generated CRFs for Morphological Analysis of Noisy ECG Data<\/td>\r\n Annamalai Natarajan \/ Annamalai Natarajan, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, Benjamin Marlin<\/td>\r\n<\/tr>\r\n \r\n Generative and Discriminative Models for Improving Noisy Training Data for Relation Extraction<\/td>\r\n Benjamin Roth \/ Benjamin Roth, Dietrich Klakow<\/td>\r\n<\/tr>\r\n \r\n Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations<\/td>\r\n Bilal Ahmed \/ Bilal Ahmed, Thomas Thesen, Karen Blackmon, Orrin Devinsky, Ruben Kuzniecky, and Carla E. Brodley<\/td>\r\n<\/tr>\r\n \r\n Boundary algorithm for fast online classification and regression<\/td>\r\n Charles Mathy \/ Charles Mathy, Nate Derbinsky, Jose Bento, Jonathan Rosenthal, Jonathan Yedidia<\/td>\r\n<\/tr>\r\n \r\n Best Response Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty<\/td>\r\n Chris Amato \/ Frans A. Oliehoek and Christopher Amato<\/td>\r\n<\/tr>\r\n \r\n Learning Dirichlet Priors for Affordance Aware Planning<\/td>\r\n David Abel and Gabriel Barth-Maron \/ David Abel, Gabriel Barth-Maron, James MacGlashan, Stefanie Tellex<\/td>\r\n<\/tr>\r\n \r\n Learning with Mixtures of Dependency Networks<\/td>\r\n David Arbour \/ David Arbour, David Jensen<\/td>\r\n<\/tr>\r\n \r\n Employment of Frank-Wolfe algorithm to perform marginal inference in a Gibbs distribution<\/td>\r\n David Belanger<\/td>\r\n<\/tr>\r\n \r\n Restricted Memory Online Variational Bayesian Changepoint Detection<\/td>\r\n Diana Cai \/ Diana Cai, Ryan Adams<\/td>\r\n<\/tr>\r\n \r\n Learning Modular Structures from Network Data and Node Variables<\/td>\r\n Elham Azizi \/ Elham Azizi, Edoardo M. Airoldi, James E. Galagan<\/td>\r\n<\/tr>\r\n \r\n Dynamic Statistical Models of Collective Social Network Behavior<\/td>\r\n Elisabeth Baseman \/ Elisabeth Baseman, Stephen Judd, Michael Kearns, David Jensen<\/td>\r\n<\/tr>\r\n \r\n Fast Margin-based Cost-sensitive Classification<\/td>\r\n Feng Nan \/ Feng Nan, Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama<\/td>\r\n<\/tr>\r\n \r\n Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition<\/td>\r\n Hanie Sedghi<\/td>\r\n<\/tr>\r\n \r\n Augur: a Modeling Language for Data-Parallel Probabilistic Inference<\/td>\r\n Jean-Baptiste Tristan \/ Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr<\/td>\r\n<\/tr>\r\n \r\n Connected Sub-graph Detection<\/td>\r\n Jing Qian \/ Jing Qian, Venkatesh Saligrama, Yuting Chen<\/td>\r\n<\/tr>\r\n \r\n An information-theoretic analysis of resampling in sequential Monte Carlo<\/td>\r\n Jonathan Huggins \/ Jonathan H. Huggins and Daniel M. Roy<\/td>\r\n<\/tr>\r\n \r\n Text analysis techniques for nominating contact offenders in peer-to-peer file sharing networks<\/td>\r\n Juston Moore \/ Juston Moore, Brian Levine, Marc Liberatore, Hanna Wallach, Janis Wolak<\/td>\r\n<\/tr>\r\n \r\n A Sound and Complete Algorithm for Learning Causal Models from Relational Data<\/td>\r\n Katerina Marazopoulou \/ Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen<\/td>\r\n<\/tr>\r\n \r\n Time Series Analysis of Mobile Data Usage Reveals Geographic Location<\/td>\r\n Keen Sung \/ Keen Sung, Erik Learned-Miller, Brian Levine, Marc Liberatore<\/td>\r\n<\/tr>\r\n \r\n Evaluating Topic Models through Histogram Analysis<\/td>\r\n Kriste Krstovski \/ Kriste Krstovski, David A. Smith and Michael J. Kurtz<\/td>\r\n<\/tr>\r\n \r\n A Graphical Model for Entity-based Document Retrieval<\/td>\r\n Laura Dietz \/ Laura Dietz, Jeffrey Dalton, Bruce Croft<\/td>\r\n<\/tr>\r\n \r\n Classifier-Adjusted Density Estimation for Anomaly Detection and One-Class<\/td>\r\n Lisa Friedland \/ Lisa Friedland, Amanda Gentzel, David Jensen<\/td>\r\n<\/tr>\r\n \r\n Learning of Overcomplete Latent Variable Models: Supervised and Semi-supervised Settings<\/td>\r\n Majid Janzamin<\/td>\r\n<\/tr>\r\n \r\n Tensor Factorization for Large-Scale Relational Learning<\/td>\r\n Maximilian Nickel \/ Maximilian Nickel, Volker Tresp<\/td>\r\n<\/tr>\r\n \r\n Person Re-Identification using Kernel-based Metric Learning Methods<\/td>\r\n Mengran Gou \/ Fei Xiong, Mengran Gou, Octavia Camps, Mario Sznaier<\/td>\r\n<\/tr>\r\n \r\n Regression with No Labeled Data<\/td>\r\n Mohammad Gheshlaghi Azar \/ Mohammad Gheshlaghi Azar and Konrad Kording<\/td>\r\n<\/tr>\r\n \r\n Inferring Helpful Actions<\/td>\r\n Nakul Gopalan \/ Nakul Gopalan, Izaak Baker, Stefanie Tellex<\/td>\r\n<\/tr>\r\n \r\n DISCOMAX: Distance Correlation Maximization using Graph Laplacians<\/td>\r\n Praneeth Vepakomma \/ Praneeth Vepakomma, Chetan Tonde, Ahmed Elgammal<\/td>\r\n<\/tr>\r\n \r\n Towards Collaborative Filtering Recommender Systems for Tailored Health Communications<\/td>\r\n Roy Adams<\/td>\r\n<\/tr>\r\n \r\n Deterministic Feature Selection for Linear Support Vector Machines<\/td>\r\n Saurabh Paul \/ Saurabh Paul, Malik Magdon-Ismail and Petros Drineas<\/td>\r\n<\/tr>\r\n \r\n The Value of Temporal Data for Learning Influence Networks<\/td>\r\n Spyros Zoumpoulis \/ Munther Dahleh, John Tsitsiklis, Spyros Zoumpoulis<\/td>\r\n<\/tr>\r\n \r\n Co-Planning via Inverse Reinforcement Learning<\/td>\r\n Stephen Brawner \/ Stephen Brawner, Lee Painton, Stefanie Tellex, Michael Littman<\/td>\r\n<\/tr>\r\n \r\n A Kernel-Based Framework for Learning with Irregularly Sampled Physiological Time Series<\/td>\r\n Steve Li<\/td>\r\n<\/tr>\r\n \r\n Gradient-based inference for higher-order probabilistic programming languages<\/td>\r\n Tianlin Shi \/ Alexey Radul, Vikash K. Mansinghka<\/td>\r\n<\/tr>\r\n \r\n Sensing-Aware kernel SVMs<\/td>\r\n Weicong Ding \/ Weicong Ding, Prakash Ishwar, Venkatesh Saligrama, W. Clem Karl<\/td>\r\n<\/tr>\r\n \r\n Authorship attribution of unsigned Supreme Court opinions<\/td>\r\n William Li \/ William Li, Pablo Azar, David Larochelle, Phil Hill, James Cox, Robert C. Berwick, Andrew W. Lo<\/td>\r\n<\/tr>\r\n \r\n Modeling and Prediction of Heart-Related Hospitalization Using Electronic Health Records Data<\/td>\r\n Wuyang Dai \/ Wuyang Dai, Theodora Brisimi, Venkatesh Saligrama, Ioannis Ch. Paschalidis<\/td>\r\n<\/tr>\r\n \r\n Learning dynamic spatiotemporal fields using data from mobile sensors<\/td>\r\n Xiaodong Lan \/ Xiaodong Lan and Mac Schwager<\/td>\r\n<\/tr>\r\n \r\n Handling Physician Subjectivity in the Prediction of Disease Course: An Application to Multiple Sclerosis<\/td>\r\n Yijun Zhao \/ Yijun Zhao, Carla Brodley, Tanuja Chitnis, Brian C. Healy<\/td>\r\n<\/tr>\r\n \r\n A Convex Moments-based Approach to Subspace Clustering with Priors<\/td>\r\n Yin Wang \/ Yin Wang, Yongfang Cheng, Mario Sznaier, Octavia Camps<\/td>\r\n<\/tr>\r\n \r\n Formal Methods for Learning and Detection of Anomalous Behavior in Cyber-Physcial Systems<\/td>\r\n Zhaodan Kong \/ Zhaodan Kong, Austin Jones, Calin Belta<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nFor any questions, please contact MLday14@microsoft.com<\/a>."},{"id":3,"name":"Committee","content":"Ryan Adams, Computer Science, Harvard\r\nSham Kakade, Microsoft Research New England\r\nLorenzo Rosasco, Universita' di Genova and MIT\r\nStefanie Tellex, Computer Science, Brown\r\n