activity
20192025
most citedCharacterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory

34 citations · 60 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025

Improved Scalable Lipschitz Bounds for Deep Neural Networks

Usman Syed, Bin Hu

Computing tight Lipschitz bounds for deep neural networks is crucial for analyzing their robustness and stability, but existing approaches either produce relatively conservative es…

eess.SY2024★ 3 cited

ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise

Xingang Guo, Darioush Keivan, Usman Syed +5

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robo…

cs.AI2024★ 2 cited

Benchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviors

Usman Syed, Ethan Light, Xingang Guo +4

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, GPT-4o, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, Llama 3, and Ll…

math.OC2024★ 20 cited

Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra

Darioush Kevian, Usman Syed, Xingang Guo +5

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra in solving undergraduate-level contro…

math.OC2023★ 1 cited

Bounds for the Tracking Error and Dynamic Regret of Inexact Online Optimization Methods: A General Analysis via Sequential Semidefinite Programs

Usman Syed, Emiliano Dall'Anese, Bin Hu

In this paper, we develop a unified framework for analyzing the tracking error and dynamic regret of inexact online optimization methods under a variety of settings. Specifically,…

cs.LG2019★ 34 cited

Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory

Bin Hu, Usman Ahmed Syed

In this paper, we provide a unified analysis of temporal difference learning algorithms with linear function approximators by exploiting their connections to Markov jump linear sys…