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Stochastics and Statistics Seminar

Exponential line-crossing inequalities

April 12, 2019 @ 11:00 am - 12:00 pm

Aaditya Ramdas (Carnegie Mellon University)


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, Pinelis, Blackwell, van de Geer, and de la Pena; and several modern inequalities (post-2000) by Khan, Tropp, Bercu and Touati, Delyon, and others. In each of these cases, we give the strongest and most general statements to date, quantifying the time-uniform concentration of scalar, matrix, and Banach-space-valued martingales, under a variety of nonparametric assumptions in discrete and continuous time. In doing so, we bridge the gap between existing line-crossing inequalities, the sequential probability ratio test, the Cramer-Chernoff method, self-normalized processes, and other parts of the literature. Time permitting, I will briefly discuss applications to sequential covariance matrix estimation, adaptive clinical trials and multi-armed bandits via the notion of “confidence sequences”.

(joint work with Steve Howard, Jas Sekhon and Jon McAuliffe, preprint https://arxiv.org/abs/1808.03204)

Aaditya Ramdas is an assistant professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. Previously, he was a postdoctoral researcher in Statistics and EECS at UC Berkeley from 2015-18, mentored by Michael Jordan and Martin Wainwright. He finished his PhD at CMU in Statistics and Machine Learning, advised by Larry Wasserman and Aarti Singh, winning the Best Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay. A lot of his research focuses on modern aspects of reproducibility in science and technology — involving statistical testing and false discovery rate control in static and dynamic settings. He also works on some problems in sequential decision-making and online uncertainty quantification

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

MIT Statistics + Data Science Center
Massachusetts Institute of Technology
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Cambridge, MA 02139-4307