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…
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…
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…
Abstract: We consider sequential prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown constraint set. We quantify relaxations of the classical i.i.d. assumption in terms of these constraint sets, with i.i.d. sequences at one extreme and adversarial mechanisms at the other. The Hedge algorithm, long known to be minimax…
Abstract: Mainstream machine learning, despite its recent successes, has a serious drawback: while its state-of-the-art algorithms often produce excellent predictions, they do not provide measures of their accuracy and reliability that would be both practically useful and provably valid. Conformal prediction adapts rank tests, popular in nonparametric statistics, to testing the IID assumption (the observations…
Abstract: Wasserstein barycenters have become a central object in applied optimal transport as a tool to summarize complex objects that can be represented as distributions. Such objects include posterior distributions in Bayesian statistics, functions in functional data analysis and images in graphics. In a nutshell a Wasserstein barycenter is a probability distribution that provides a…
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…
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…
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…
Abstract: We consider sequential prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown constraint set. We quantify relaxations of the classical i.i.d. assumption in terms of these constraint sets, with i.i.d. sequences at one extreme and adversarial mechanisms at the other. The Hedge algorithm, long known to be minimax…
Abstract: Mainstream machine learning, despite its recent successes, has a serious drawback: while its state-of-the-art algorithms often produce excellent predictions, they do not provide measures of their accuracy and reliability that would be both practically useful and provably valid. Conformal prediction adapts rank tests, popular in nonparametric statistics, to testing the IID assumption (the observations…
Abstract: Wasserstein barycenters have become a central object in applied optimal transport as a tool to summarize complex objects that can be represented as distributions. Such objects include posterior distributions in Bayesian statistics, functions in functional data analysis and images in graphics. In a nutshell a Wasserstein barycenter is a probability distribution that provides a…
