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...
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...
Limits of Local Algorithms for Randomly Generated Constraint Satisfaction Problems
(sponsored jointly with the Statistics and Probability Seminar Series)
