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  • Locality and Message Passing in Network Optimization

    32-141 , United States

    The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. 

  • Estimating High-Dimensional Autoregressive Point Processes

    32-141 , United States

    The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. 

  • Shotgun Assembly of Graphs

    25-111 , United States

    The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability, statistics, optimization, and applied mathematics. The seminar also regularly features…

  • LIDS Student Conference

    32-141 , United States

    This a student-organized, student-run conference provides an opportunity for graduate students to present their research to peers, as well as to the community at large.

  • Women in Data Science (WiDS) Conference

    Microsoft NERD Center, Cambridge , United States

    This conference will bring together local academic leaders, industrial professionals, and students to hear about the latest data science-related research in a number of domains; to learn how companies are leveraging data science for success; and to connect with potential mentors, collaborators, and others in the field.

  • Non-classical Berry-Esseen inequality and accuracy of the weighted bootstrap

    E18-304 , United States

    The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability, statistics, optimization, and applied mathematics. The seminar also regularly features…

  • Slope meets Lasso in sparse linear regression

    E18-304 , United States

    The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability, statistics, optimization, and applied mathematics. The seminar also regularly features…

  • Towards a Theory of Fairness in Machine Learning

    32-G449 (Kiva) , United States

    Abstract:  Algorithm design has moved from being a tool used exclusively for designing systems to one used to present people with personalized content, advertisements, and other economic opportunities. Massive amounts of information is recorded about people's online behavior including the websites they visit, the advertisements they click on, their search history, and their IP address. Algorithms then use this information…

  • An Information Theoretic Perspective on the ExplorationExploitation Tradeoff

    32-141 , United States

    The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. 


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