no code implementations • 2 Nov 2023 • Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen
In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks.
no code implementations • 10 Feb 2023 • Minhui Huang, Dewei Zhang, Kaiyi Ji
However, several important properties in federated learning such as the partial client participation and the linear speedup for convergence (i. e., the convergence rate and complexity are improved linearly with respect to the number of sampled clients) in the presence of non-i. i. d.~datasets, still remain open.
no code implementations • 23 Oct 2022 • Xuxing Chen, Minhui Huang, Shiqian Ma, Krishnakumar Balasubramanian
Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning, and hyperparameter optimization.
no code implementations • 8 Feb 2022 • Minhui Huang, Xuxing Chen, Kaiyi Ji, Shiqian Ma, Lifeng Lai
Moreover, we propose an inexact NEgative-curvature-Originated-from-Noise Algorithm (iNEON), a pure first-order algorithm that can escape saddle point and find local minimum of stochastic bilevel optimization.
no code implementations • 29 Sep 2021 • 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.
no code implementations • 5 Feb 2021 • Minhui Huang, Shiqian Ma, Lifeng Lai
One of the popular solution methods for this task is to compute the barycenter of the probability measures under the Wasserstein metric.
no code implementations • 4 Feb 2021 • Minhui Huang
We propose perturbed proximal algorithms that can provably escape strict saddles for nonsmooth weakly convex functions.
no code implementations • 9 Dec 2020 • Minhui Huang, Shiqian Ma, Lifeng Lai
We show that the complexity of arithmetic operations for RBCD to obtain an $\epsilon$-stationary point is $O(\epsilon^{-3})$.
no code implementations • 18 Aug 2020 • Minhui Huang, Shiqian Ma, Lifeng Lai
This problem aims to decompose a partially observed matrix into the superposition of a low-rank matrix and a sparse matrix, where the sparse matrix captures the grossly corrupted entries of the matrix.