Past Events

  • 2019 Mar 06

    STAT 300: Wenshuo Wang

    12:00pm to 1:00pm

    Location: 

    SC 705

    Title: Metropolized Knockoff Sampling

    Abstract: Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This paper introduces techniques for knockoff generation in great...

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  • 2019 Feb 20

    STAT 300: Assistant Professor Pierre Jacob

    12:00pm to 1:00pm

    Location: 

    SC 705

    Title: Recent developments on unbiased Monte Carlo methods

    Abstract: Monte Carlo estimators, based on Markov chains or interacting particle systems, are typically biased when run with a finite number of iterations (or a finite number of particles). Although this is usually considered unavoidable, and negligible in the usual asymptotic sense, it is an important obstacle on the path towards scalable numerical integration on large-scale distributed computing systems. In a series of works that build on the seminal paper of Glynn and Rhee (2014), a...

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  • 2019 Feb 11

    Qingyuan Zhao: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Mendelian randomization: A comprehensive statistical approach and applications to preventing heart disease

    Mendelian randomization (MR) can give unbiased estimate of a confounded causal effect by using genetic variants as instrumental variables. The summary-data MR design is rapidly gaining popularity in practice due to the increasing availability of large-scale genome-wide association studies. As we are entering the "MR of every risk factor on every disease outcome" era, existing statistical methods still have several major limitations and lack theoretical...

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  • 2019 Feb 08

    Yuansi Chen: Job Talk

    12:00pm to 1:00pm

    Location: 

    Science Center 300H

    Stability-driven deep model interpretation and provably fast MCMC sampling

    Data science is transforming many traditional ways in which we approach scientific problems. While the abundance of data and algorithms generate a lot of excitement in statistical modeling, serious concerns about how to reliably and efficiently extract scientific knowledge from data and models are being raised.

    In this talk, I will address particular reliability and efficiency issues that arise from my PhD study on a neuroscience project. Understanding how primates process...

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