6 papers
Approximate Risk Minimization Over Shrinking-Thresholding Rules in Normal Mean Estimation
Wei Jiang
We develop an approximate risk minimization framework for shrinkage-thresholding estimation in normal mean problems. In the canonical multivariate normal mean model, we introduce a…
Mirror Descent Under Generalized Smoothness
Dingzhi Yu, Wei Jiang, Hongyi Tao +2
Smoothness is crucial for attaining fast rates in first-order optimization. However, many optimization problems in modern machine learning involve non-smooth objectives. Recent stu…
Distributed Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower bounds
Sifan Yang, Wenhao Yang, Wei Jiang +1
We investigate distributed online convex optimization with compressed communication, where learners connected by a network collaboratively minimize a sequence of global loss fu…
Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings
Wei Jiang, Lijun Zhang
In this paper, we analyze the convergence properties of the Lion optimizer. First, we establish that the Lion optimizer attains a convergence rate of …
Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions
Lijun Zhang, Wenhao Yang, Guanghui Wang +2
To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the…
Improved Analysis for Sign-based Methods with Momentum Updates
Wei Jiang, Dingzhi Yu, Sifan Yang +2
In this paper, we present enhanced analysis for sign-based optimization algorithms with momentum updates. Traditional sign-based methods, under the separable smoothness assumption,…