Date & Time:
November 22, 2019 10:30 am – 11:30 am
Location:
TTIC 526, 6045 S. Kenwood Ave., Chicago, IL,
11/22/2019 10:30 AM 11/22/2019 11:30 AM America/Chicago Ramya Vinayak (Washington) – Learning From Sparse Data TTIC 526, 6045 S. Kenwood Ave., Chicago, IL,

Learning From Sparse Data

In many scientific domains, the number of individuals in the population under study is often very large, however the number of observations available per individual is often very limited (sparse). Limited observations prohibit accurate estimation of parameters of interest for any given individual. In this sparse data regime, the key question is, how accurately can we estimate the distribution of parameters over the population?  This problem arises in various domains such as epidemiology, psychology, health care, biology, and social sciences. As an example, suppose for a large random sample of the population we have observations of whether a person caught the flu for each year over the past 5 years. We cannot accurately estimate the probability of any given person catching the flu with only 5 observations; however, our goal is to estimate the distribution of these probabilities over the whole population. Such an estimated distribution can be used in downstream tasks, like testing and estimating properties of the distribution.

In this talk, I will present our recent results where we show that the maximum likelihood estimator (MLE) is minimax optimal in the sparse observation regime. While the MLE for this problem was proposed as early as the late 1960’s, how accurately the MLE recovers the true distribution was not known. Our work closes this gap. In the course of our analysis, we provide novel bounds on the coefficients of Bernstein polynomials approximating Lipschitz-1 functions. Furthermore, the MLE is also efficiently computable in this setting and we evaluate the performance of MLE on both synthetic and real datasets.

Joint work with Weihao Kong, Gregory Valiant, and Sham Kakade

Host: Rebecca Willett

Ramya Vinayak

Postdoctoral Researcher, University of Washington

Ramya Korlakai Vinayak is a postdoctoral researcher at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, working with Sham Kakade. Her research interests broadly span the areas of machine learning, statistical inference, and crowdsourcing. She received a Ph.D. from Caltech where she was advised by Babak Hassibi. She is a recipient of the Schlumberger Foundation Faculty of the Future fellowship from 2013- 15. She obtained her Masters from Caltech and Bachelors from IIT Madras

Related News & Events

UChicago CS News

UChicago Partners With UMass On NSF Expedition To Elevate Computational Decarbonization As A New Field In Computing

May 23, 2024
UChicago CS News

Assistant Professor Raul Castro Fernandez Awarded NSF CAREER Grant to investigate Data-sharing Markets

May 21, 2024
UChicago CS News

Empowering Middle School Girls in Tech: compileHER’s <prompt/HER> Capstone Event

May 20, 2024
UChicago CS News

Haifeng Xu Wins Best Paper Award at Leading AI Conference for Pioneering Research on Mechanism Design for LLMs

May 17, 2024
UChicago CS News

Fred Chong Receives Quantrell Award for Excellence in Teaching

May 16, 2024
UChicago CS News

Unveiling Attention Receipts: Tangible Reflections on Digital Consumption

May 15, 2024
UChicago CS News

NASA to Launch UChicago Undergraduates’ Satellite

May 14, 2024
UChicago CS News

University of Chicago Computer Science Researchers To Present Ten Papers at CHI 2024

May 06, 2024
UChicago CS News

Two UChicago MPCS Students Win the Apple Swift Student Challenge

May 01, 2024
In the News

How Artificial Intelligence Can Transform U.S. Energy Infrastructure

Apr 29, 2024
In the News

Community Data Fellow Stephania Tello Zamudio helps broaden internet access for Illinois residents

Apr 29, 2024
UChicago CS News

Two UChicago CS Students Awarded NSF Graduate Research Fellowship

Apr 24, 2024
arrow-down-largearrow-left-largearrow-right-large-greyarrow-right-large-yellowarrow-right-largearrow-right-smallbutton-arrowclosedocumentfacebookfacet-arrow-down-whitefacet-arrow-downPage 1CheckedCheckedicon-apple-t5backgroundLayer 1icon-google-t5icon-office365-t5icon-outlook-t5backgroundLayer 1icon-outlookcom-t5backgroundLayer 1icon-yahoo-t5backgroundLayer 1internal-yellowinternalintranetlinkedinlinkoutpauseplaypresentationsearch-bluesearchshareslider-arrow-nextslider-arrow-prevtwittervideoyoutube