3 papers
cs.CL2025
How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
Ayeong Lee, Ethan Che, Tianyi Peng
Chain-of-thought prompting has emerged as a powerful technique for enabling large language models (LLMs) to solve complex reasoning tasks. However, these reasoning chains can be ve…
cs.LG2024
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Haozhe Chen, Ang Li, Ethan Che +3
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare. Compared to stan…
cs.LG2024
Stochastic Gradient Descent with Adaptive Data
Ethan Che, Jing Dong, Xin T. Tong
Stochastic gradient descent (SGD) is a powerful optimization technique that is particularly useful in online learning scenarios. Its convergence analysis is relatively well underst…