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April 2019

SDSCon 2019 – Statistics and Data Science Conference

April 5
MIT Media Lab Multi-Purpose Room: E14-674

SDSCon 2019 is the third annual celebration of the statistics and data science community at MIT and beyond, organized by MIT’s Statistics and Data Science Center (SDSC).

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Exponential line-crossing inequalities

Aaditya Ramdas (Carnegie Mellon University)

April 12 @ 11:00 am - 12:00 pm
E18-304

Abstract: This talk will present a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities in this way. We will illustrate this point by presenting a single assumption and a single theorem that together strengthen many tail bounds for martingales, including classical inequalities (1960-80) by Bernstein, Bennett, Hoeffding, and Freedman; contemporary inequalities (1980-2000) by Shorack and Wellner,…

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Stochastics and Statistics Seminar

Aaron Roth (University of Pennsylvania)

April 19 @ 11:00 am - 12:00 pm
E18-304

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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Stochastics and Statistics Seminar Series

Chao Gao (University of Chicago)

April 26 @ 11:00 am - 12:00 pm
E18-304

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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May 2019

Stochastics and Statistics Seminar

Tracy Ke (Harvard University)

May 3 @ 11:00 am - 12:00 pm
E18-304

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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Counting and sampling at low temperatures

Will Perkins (University of Illinois at Chicago)

May 10 @ 11:00 am - 12:00 pm
E18-304

Abstract: We consider the problem of efficient sampling from the hard-core and Potts models from statistical physics. On certain families of graphs, phase transitions in the underlying physics model are linked to changes in the performance of some sampling algorithms, including Markov chains. We develop new sampling and counting algorithms that exploit the phase transition phenomenon and work efficiently on lattices (and bipartite expander graphs) at sufficiently low temperatures in the phase coexistence regime. Our algorithms are based on Pirogov-Sinai…

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