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[theory-seminar] Talk "Generalized Resilience and Robust Statistics" by Jiantao Jiao (Fr, 11-Oct @ 1:15pm)

Joachim Neu jneu at
Fri Oct 11 09:29:44 PDT 2019

Reminder: This takes place today, Fri, 11 Oct, 1:15pm, Packard 202.

On Sun, 2019-09-29 at 22:12 -0700, Moses Charikar wrote:
> The ISL talk this coming Friday is likely of interest to many folks
> on this list.
> Cheers,
> Moses
> ---------- Forwarded message ---------
> From: ISL Colloquium and Talk Announcements <
> isl-colloq at>
> Date: Sun, Sep 29, 2019 at 7:24 PM
> Subject: [isl-colloq] Talk "Generalized Resilience and Robust
> Statistics" by Jiantao Jiao (Fr, 11-Oct @ 1:15pm)
> To: <information_theory_forum at>, isl-colloq <
> isl-colloq at>, ee-students-forum <
> ee-students-forum at>
> Speaker: Jiantao Jiao -- Assistant Professor, UC Berkeley
> Title: Generalized Resilience and Robust Statistics
> When: Friday, 11-Oct-2019, 1:15pm to 2:15pm
> Where: Packard 202
> Abstract:
>  Robust statistics traditionally focuses on outliers, or
> perturbations
> in total variation distance. However, a dataset could be corrupted in
> many other ways, such as systematic measurement errors and missing
> covariates. We generalize the robust statistics approach to consider
> perturbations under any Wasserstein distance, and show that robust
> estimation is possible whenever a distribution’s population
> statistics
> are robust under a certain family of friendly perturbations. This
> generalizes a property called resilience previously employed in the
> special case of mean estimation with outliers. We justify the
> generalized resilience property by showing that it holds under moment
> or hypercontractive conditions. Even in the total variation case,
> these
> subsume conditions in the literature for mean estimation, regression,
> and covariance estimation; the resulting analysis simplifies and
> sometimes improves these known results in both population limit and
> finite-sample rate. Our robust estimators are based on minimum
> distance
> (MD) functionals (Donoho and Liu, 1988), which project onto a set of
> distributions under a discrepancy related to the perturbation. We
> present two approaches for designing MD estimators with good finite-
> sample rates: weakening the discrepancy and expanding the set of
> distributions. We also present connections to Gao et al. (2019)’s
> recent analysis of generative adversarial networks for robust
> estimation.
> Joint work with Banghua Zhu and Jacob Steinhardt 
> Bio:
> Jiantao Jiao is an Assistant Professor in the Department of
> Electrical Engineering and Computer Sciences and Department of
> Statistics at University of California, Berkeley. He received his
> B.Eng. degree in Electronic Engineering from Tsinghua University,
> Beijing, China in 2012, and his M.Sc. and Ph.D. degrees in Electrical
> Engineering from Stanford University in 2014 and 2018, respectively.
> He is a recipient of the Presidential Award of Tsinghua University
> and the Stanford Graduate Fellowship. He was a semi-plenary speaker
> at ISIT 2015 and a co-recipient of the ISITA 2016 Student Paper Award
> and MobiHoc 2019 best paper award. His research interests are in
> statistical machine learning, high-dimensional and nonparametric
> statistics, mathematical programming, applied probability,
> information theory, and their applications. 
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