257 citations · 384 across the 11 of their papers we have counts for
12 papers
On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization
Yuxuan Xia, Xiao-Tong Yuan
Must the dimension and accuracy costs of zeroth-order optimization multiply? Global upper bounds for smooth stochastic nonconvex optimization scale as , with both facto…
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation
Xiao-Tong Yuan, Ping Li
The stochastic proximal point (SPP) methods have gained recent attention for stochastic optimization, with strong convergence guarantees and superior robustness to the classic stoc…
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
William de Vazelhes, Hualin Zhang, Huimin Wu +2
constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Har…
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond
Xiao-Tong Yuan, Ping Li
The FedProx algorithm is a simple yet powerful distributed proximal point optimization method widely used for federated learning (FL) over heterogeneous data. Despite its popularit…
Boosting the Confidence of Generalization for -Stable Randomized Learning Algorithms
Xiao-Tong Yuan, Ping Li
Exponential generalization bounds with near-tight rates have recently been established for uniformly stable learning algorithms. The notion of uniform stability, however, is string…
Stability and Risk Bounds of Iterative Hard Thresholding
Xiao-Tong Yuan, Ping Li
In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and s…
Generalization Bounds for High-dimensional M-estimation under Sparsity Constraint
Xiao-Tong Yuan, Ping Li
The -constrained empirical risk minimization (-ERM) is a promising tool for high-dimensional statistical estimation. The existing analysis of -ERM estimator…
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
Xiao-Tong Yuan, Ping Li
The DANE algorithm is an approximate Newton method popularly used for communication-efficient distributed machine learning. Reasons for the interest in DANE include scalability and…
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization
Xiao-Tong Yuan, Ping Li, Tong Zhang
Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedure for finding sparse solutions of underdetermined linear systems. This method has been shown to have strong…
Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics
Xiao-Tong Yuan, Ping Li, Tong Zhang
We investigate a generic problem of learning pairwise exponential family graphical models with pairwise sufficient statistics defined by a global mapping function, e.g., Mercer ker…
Partial Gaussian Graphical Model Estimation
Xiao-Tong Yuan, Tong Zhang
This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using -…
Truncated Power Method for Sparse Eigenvalue Problems
Xiao-Tong Yuan, Tong Zhang
This paper considers the sparse eigenvalue problem, which is to extract dominant (largest) sparse eigenvectors with at most non-zero components. We propose a simple yet effecti…