14 citations · 40 across the 10 of their papers we have counts for
10 papers
LLM-Powered AI Agent Systems and Their Applications in Industry
Guannan Liang, Qianqian Tong
The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility…
Meta-Shop: Improving Item Advertisement For Small Businesses
Yang Shi, Guannan Liang, Young-joo Chung
In this paper, we study item advertisements for small businesses. This application recommends prospective customers to specific items requested by businesses. From analysis, we fou…
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods
Guannan Liang, Qianqian Tong, Chunjiang Zhu +1
Stochastically controlled stochastic gradient (SCSG) methods have been proved to converge efficiently to first-order stationary points which, however, can be saddle points in nonco…
An Efficient Algorithm for Deep Stochastic Contextual Bandits
Tan Zhu, Guannan Liang, Chunjiang Zhu +2
In stochastic contextual bandit (SCB) problems, an agent selects an action based on certain observed context to maximize the cumulative reward over iterations. Recently there have…
Federated Nonconvex Sparse Learning
Qianqian Tong, Guannan Liang, Tan Zhu +1
Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-…
Asynchronous Parallel Stochastic Quasi-Newton Methods
Qianqian Tong, Guannan Liang, Xingyu Cai +2
Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-…
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning
Guannan Liang, Qianqian Tong, Jiahao Ding +2
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential r…
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data
Qianqian Tong, Guannan Liang, Jinbo Bi
Federated learning allows loads of edge computing devices to collaboratively learn a global model without data sharing. The analysis with partial device participation under non-IID…
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
Jiahao Ding, Jingyi Wang, Guannan Liang +2
The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local p…
Calibrating the Adaptive Learning Rate to Improve Convergence of ADAM
Qianqian Tong, Guannan Liang, Jinbo Bi
Adaptive gradient methods (AGMs) have become popular in optimizing the nonconvex problems in deep learning area. We revisit AGMs and identify that the adaptive learning rate (A-LR)…