6 accepted papers
Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes. Despite its advantages, such data collection mechanism often introduces complexities to the statistical inference procedure. For instance, the ordinary least squares (OLS) e…
Estimation and inference in statistics pose significant challenges when data are collected adaptively. Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to exhibit asymptotic normality for single coordinate estimation and have inflated error. This issue is highlighted by a r…
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i.i.d. samples are known to have lower accuracy than the classical $n^{- \frac{1}{2}}$ error. We investigate whether the Expectation-Maximization (EM) algorithm also converge…
We study derivative-free methods for policy optimization over the class of linear policies. We focus on characterizing the convergence rate of a canonical stochastic, two-point, derivative-free method for linear-quadratic systems in which the initial state of the system is drawn at random. In partic…
Non-convex optimization problems arise frequently in machine learning, including feature selection, structured matrix learning, mixture modeling, and neural network training. We consider the problem of finding critical points of a broad class of non-convex problems with non-smooth components. We ana…
Recent years have witnessed substantial progress in understanding the behavior of EM for mixture models that are correctly specified. Given that model misspecification is common in practice, it is important to understand EM in this more general setting. We provide non-asymptotic guarantees…