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Capacity lower bound for the Ising perceptron
Nike Sun (MIT)
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 joint work with Jian Ding. Biography: Nike Sun is a faculty…
Find out more »Why Aren’t Network Statistics Accompanied By Uncertainty Statements?
Eric Kolaczyk (Boston University)
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…
Find out more »Women in Data Science (WiDS) – Cambridge, MA
This one-day technical 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 leading-edge companies are leveraging data science for success, and to connect with potential mentors, collaborators, and others in the field.
Find out more »IDSS Distinguished Speaker Seminar – Sanjeev Arora
Sanjeev Arora (Princeton University)
32-155
Detailed information will be posted soon. About the Speaker: Sanjeev Arora is the Charles C. Fitzmorris Professor in Computer Science. He joined Princeton in 1994 after earning his doctorate from the University of California, Berkeley. He was a visiting professor at the Weizmann Institute in 2007, a visiting researcher at Microsoft in 2006-07, and a visiting associate professor at Berkeley during 2001-02. Professor Arora’s honors include the D.R. Fulkerson Prize in Discrete Mathematics (awarded by the American Mathematical Society and…
Find out more »Univariate total variation denoising, trend filtering and multivariate Hardy-Krause variation denoising
Aditya Guntuboyina (UC Berkley)
E18-304
Abstract: Total variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted…
Find out more »Stochastics and Statistics Seminar Series
Eliran Subag (Nw York University)
E18-304
A Particulate Solution: Data Science in the Fight to Stop Air Pollution and Climate Change | IDSS Distinguished Speaker Seminar
Francesca Dominici (Harvard University)
32-155
Abstract: What if I told you I had evidence of a serious threat to American national security – a terrorist attack in which a jumbo jet will be hijacked and crashed every 12 days. Thousands will continue to die unless we act now. This is the question before us today – but the threat doesn’t come from terrorists. The threat comes from climate change and air pollution. We have developed an artificial neural network model that uses on-the-ground air-monitoring data…
Find out more »SDSCon2019
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).
Find out more »Exponential line-crossing inequalities
Aaditya Ramdas (Carnegie Mellon University)
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,…
Find out more »Stochastics and Statistics Seminar Series
Aaron Roth (University of Pennsylvania)
E18-304