18 citations · 26 across the 10 of their papers we have counts for
10 papers
Optimality and computational barriers in variable selection under dependence
Ming Gao, Bryon Aragam
We study the optimal sample complexity of variable selection in linear regression under general design covariance, and show that subset selection is optimal while under standard co…
Optimal structure learning and conditional independence testing
Ming Gao, Yuhao Wang, Bryon Aragam
We establish a fundamental connection between optimal structure learning and optimal conditional independence testing by showing that the minimax optimal rate for structure learnin…
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…
Decentralized Local Updates with Dual-Slow Estimation and Momentum-based Variance-Reduction for Non-Convex Optimization
Kangyang Luo, Kunkun Zhang, Shengbo Zhang +2
Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require e…
KL-BSS: Rethinking optimality for neighbourhood selection in structural equation models
Ming Gao, Wai Ming Tai, Bryon Aragam
We introduce a new method for neighbourhood selection in linear structural equation models that improves over classical methods such as best subset selection (BSS) and the Lasso. O…
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…
Efficient Bayesian network structure learning via local Markov boundary search
Ming Gao, Bryon Aragam
We analyze the complexity of learning directed acyclic graphical models from observational data in general settings without specific distributional assumptions. Our approach is inf…
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Goutham Rajendran, Bohdan Kivva, Ming Gao +1
Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning direc…
A polynomial-time algorithm for learning nonparametric causal graphs
Ming Gao, Yi Ding, Bryon Aragam
We establish finite-sample guarantees for a polynomial-time algorithm for learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is mode…
QuanFuzz: Fuzz Testing of Quantum Program
Jiyuan Wang, Ming Gao, Yu Jiang +4
Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from tradit…