30 citations · 53 across the 9 of their papers we have counts for
9 papers
Achieving Linear Speedup in Non-IID Federated Bilevel Learning
Minhui Huang, Dewei Zhang, Kaiyi Ji
Federated bilevel optimization has received increasing attention in various emerging machine learning and communication applications. Recently, several Hessian-vector-based algorit…
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
Xuxing Chen, Minhui Huang, Shiqian Ma +1
Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning,…
Decentralized Bilevel Optimization
Xuxing Chen, Minhui Huang, Shiqian Ma
Bilevel optimization has been successfully applied to many important machine learning problems. Algorithms for solving bilevel optimization have been studied under various settings…
Efficiently Escaping Saddle Points in Bilevel Optimization
Minhui Huang, Xuxing Chen, Kaiyi Ji +2
Bilevel optimization is one of the fundamental problems in machine learning and optimization. Recent theoretical developments in bilevel optimization focus on finding the first-ord…
On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport
Minhui Huang, Shiqian Ma, Lifeng Lai
This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc. In…
Projection Robust Wasserstein Barycenters
Minhui Huang, Shiqian Ma, Lifeng Lai
Collecting and aggregating information from several probability measures or histograms is a fundamental task in machine learning. One of the popular solution methods for this task…
Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
Minhui Huang, Weiming Zhu
We propose perturbed proximal algorithms that can provably escape strict saddles for nonsmooth weakly convex functions. The main results are based on a novel characterization of $ε…
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
Minhui Huang, Shiqian Ma, Lifeng Lai
The Wasserstein distance has become increasingly important in machine learning and deep learning. Despite its popularity, the Wasserstein distance is hard to approximate because of…
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method
Minhui Huang, Shiqian Ma, Lifeng Lai
Robust low-rank matrix completion (RMC), or robust principal component analysis with partially observed data, has been studied extensively for computer vision, signal processing an…