5 citations · 6 across the 7 of their papers we have counts for
10 papers
A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents
Sheldon Yu, Yingcheng Sun, Hanqing Guo +1
Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning. However, their increasing autonomy also…
GEM-Style Constraints for PEFT with Dual Gradient Projection in LoRA
Brian Tekmen, Jason Yin, Qianqian Tong
Full fine-tuning of Large Language Models (LLMs) is computationally costly, motivating Continual Learning (CL) approaches that utilize parameter-efficient adapters. We revisit Grad…
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…
Stochastic Variance-Reduced Iterative Hard Thresholding in Graph Sparsity Optimization
Derek Fox, Samuel Hernandez, Qianqian Tong
Stochastic optimization algorithms are widely used for large-scale data analysis due to their low per-iteration costs, but they often suffer from slow asymptotic convergence caused…
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…
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-…