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Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models
Raaz Dwivedi, Nhat Ho, Koulik Khamaru +3
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on i.i.d. samples are known to have lower accuracy than t…
Singularity, Misspecification, and the Convergence Rate of EM
Raaz Dwivedi, Nhat Ho, Koulik Khamaru +3
A line of recent work has analyzed the behavior of the Expectation-Maximization (EM) algorithm in the well-specified setting, in which the population likelihood is locally strongly…
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel +1
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can ta…
Statistical guarantees for the EM algorithm: From population to sample-based analysis
Sivaraman Balakrishnan, Martin J. Wainwright, Bin Yu
We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a…
The geometry of kernelized spectral clustering
Geoffrey Schiebinger, Martin J. Wainwright, Bin Yu
Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function…
Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression
Hanzhong Liu, Bin Yu
We study the asymptotic properties of Lasso+mLS and Lasso+Ridge under the sparse high-dimensional linear regression model: Lasso selecting predictors and then modified Least Square…