Developed by 10 MIT faculty members at IDSS, this seven-week course is specially designed for data scientist, business analyst, engineers and technical managers looking to learn the latest theories and strategies to harness data. Offered by MIT xPRO.
Please join us on Tuesday, February 2, 2021 at 3:00pm for the Distinguished Speaker Seminar with Nathan Nunn, Frederic E. Abbe Professor of Economics at Harvard University.
The MIT Sports Lab invites you to the MIT Sports Summit 2021, a virtual event hosted on Thursday, Feb. 4th and Friday, Feb. 5th! It is an opportunity for the MIT community to interface with the Sports Lab’s affiliates and partners, sharing advances, challenges, and passions at the intersection of engineering and sports. We are featuring talks from leaders in industry and academia, as well as interactive sessions showcasing student research posters and sports tech startups. This is an invitation-only event for current MIT community…
Abstract: The goal of list learning is to understand how to learn basic statistics of a dataset when it has been corrupted by an overwhelming fraction of outliers. More formally, one is given a set of points $S$, of which an $\alpha$-fraction $T$ are promised to be well-behaved. The goal is then to output an $O(1 / \alpha)$ sized list of candidate means, so that one of these candidates is close to the true mean of the points in $T$.…
Abstract: Introduced by Kiefer and Wolfowitz 1956, the nonparametric maximum likelihood estimator (NPMLE) is a widely used methodology for learning mixture models and empirical Bayes estimation. Sidestepping the non-convexity in mixture likelihood, the NPMLE estimates the mixing distribution by maximizing the total likelihood over the space of probability measures, which can be viewed as an extreme form of over parameterization. In this work we discover a surprising property of the NPMLE solution. Consider, for example, a Gaussian mixture model on…
Please join us on Tuesday, March 2, 2021 at 3:00pm for the Distinguished Speaker Seminar with Brigitte Madrian (Brigham Young University)
Join this joint webinar on March 4th to learn more about this blended learning Masters' program offered by UTEC (Universidad Tecnológica del Uruguay) with the academic support of the Institute for Data, Systems, and Society.
Abstract: Two of the fundamental problems in non-parametric statistical inference are goodness-of-fit and two-sample testing. These two problems have been extensively studied and several multivariate tests have been proposed over the last thirty years, many of which are based on geometric graphs. These include, among several others, the celebrated Friedman-Rafsky two-sample test based on the minimal spanning tree and the K-nearest neighbor graphs, and the Bickel-Breiman spacings tests for goodness-of-fit. These tests are asymptotically distribution-free, universally consistent, and computationally efficient…
For the fifth year in a row, Harvard, MIT, Microsoft Research New England, and Broad Institute are proud to collaborate with Stanford University to bring the Women in Data Science (WiDS) conference to Cambridge, Massachusetts. This virtual, one-day technical conference will feature an all-female line up of speakers from academia and industry to talk 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,…
Abstract: For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators ψ̂ 1 are the state-of-the-art, incorporating the good prediction performance of machine learning; the decreased bias of doubly robust estimators; and the analytic tractability and bias reduction of sample splitting with cross fitting. Nonetheless, even in the absence of confounding by unmeasured factors, the nominal (1−α) Wald confidence interval ψ̂ 1±zα/2ˆ may still undercover even in large samples, because the bias of ψ̂ 1 may be of the same…
