1 citations · 1 across the 8 of their papers we have counts for
8 papers
Active Regression for Single-Index Models with Unknown Link Functions
Chansophea Wathanak In, Yi Li, Wai Ming Tai +1
This paper studies active regression for single-index models under general -loss with an unknown -Lipschitz link function , formulated as …
Dimension-independent rates for structured neural density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applic…
Breaking the curse of dimensionality in structured density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We consider the problem of estimating a structured multivariate density, subject to Markov conditions implied by an undirected graph. In the worst case, without Markovian assumptio…
Agnostic Active Learning of Single Index Models with Linear Sample Complexity
Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu +3
We study active learning methods for single index models of the form , where and ${\mathb…
Optimal estimation of Gaussian (poly)trees
Yuhao Wang, Ming Gao, Wai Ming Tai +2
We develop optimal algorithms for learning undirected Gaussian trees and directed Gaussian polytrees from data. We consider both problems of distribution learning (i.e. in KL dista…
Learning Mixtures of Gaussians with Censored Data
Wai Ming Tai, Bryon Aragam
We study the problem of learning mixtures of Gaussians with censored data. Statistical learning with censored data is a classical problem, with numerous practical applications, how…
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures
Bryon Aragam, Wai Ming Tai
We study the problem of learning nonparametric distributions in a finite mixture, and establish tight bounds on the sample complexity for learning the component distributions in su…
Optimal estimation of Gaussian DAG models
Ming Gao, Wai Ming Tai, Bryon Aragam
We study the optimal sample complexity of learning a Gaussian directed acyclic graph (DAG) from observational data. Our main results establish the minimax optimal sample complexity…