2018 - 2019

2019 Apr 12

Open House Lunch for Freshmen

11:30am to 1:30pm

Location: 

Science Center, 7th Floor

Meet faculty, eat pizza, and learn about the concentration and secondary field in Statistics!

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 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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2019 Feb 07

Pragya Sur: Job Talk

12:00pm to 1:00pm

Location: 

Science Center 300H

Title: A modern maximum-likelihood approach for high-dimensional logistic regression

Abstract: Logistic regression is arguably the most widely used and studied non-linear model in statistics. Classical maximum-likelihood theory based statistical inference is ubiquitous in this context. This theory hinges on well-known fundamental results: (1) the maximum-likelihood-estimate (MLE) is asymptotically unbiased and normally distributed, (2) its variability can be quantified via the inverse Fisher information, and (3) the...

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