1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CL2025★ 1 cited
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★ 1 cited
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…