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E18-304

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Cambridge, MA 02139

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February 2019

Capacity lower bound for the Ising perceptron

Nike Sun (MIT)

February 22 @ 11:00 am - 12:00 pm
E18-304

Abstract: The perceptron is a toy model of a simple neural network that stores a collection of given patterns. Its analysis reduces to a simple problem in high-dimensional geometry, namely, understanding the intersection of the cube (or sphere) with a collection of random half-spaces. Despite the simplicity of this model, its high-dimensional asymptotics are not well understood. I will describe what is known and present recent results. This is a joint work with Jian Ding. Biography: Nike Sun is a…

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March 2019

Why Aren’t Network Statistics Accompanied By Uncertainty Statements?

Eric Kolaczyk (Boston University)

March 1 @ 11:00 am - 12:00 pm
E18-304

Abstract: Over 500K scientific articles have been published since 1999 with the word “network” in the title. And the vast majority of these report network summary statistics of one type or another. However, these numbers are rarely accompanied by any quantification of uncertainty. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Perhaps surprisingly, there is little in the way of…

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Stochastics and Statistics Seminar

Alex Belloni (Duke University)

March 15 @ 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

Eliran Subag (New York University)

March 22 @ 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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April 2019

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 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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