Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. - Jerry Li 001; Guanghao Ye (NeurIPS, 2020)

A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path. - Sally Dong; Yin Tat Lee; Guanghao Ye (STOC, 2021)

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. - Damek Davis; Dmitriy Drusvyatskiy; Yin Tat Lee; Swati Padmanabhan; Guanghao Ye (NeurIPS, 2022)

Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. - Sally Dong; Haotian Jiang; Yin Tat Lee; Swati Padmanabhan; Guanghao Ye (NeurIPS, 2022)

Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. - Sally Dong; Yu Gao 001; Gramoz Goranci; Yin Tat Lee; Richard Peng; Sushant Sachdeva; Guanghao Ye (SODA, 2022)

Faster Min-Cost Flow on Bounded Treewidth Graphs. - Sally Dong; Guanghao Ye (CoRR, 2023)

Faster Min-Cost Flow and Approximate Tree Decomposition on Bounded Treewidth Graphs. - Sally Dong; Guanghao Ye (ESA, 2024)

Fast Algorithms for Separable Linear Programs. - Sally Dong; Gramoz Goranci; Lawrence Li; Sushant Sachdeva; Guanghao Ye (SODA, 2024)

Improving the Bit Complexity of Communication for Distributed Convex Optimization. - Mehrdad Ghadiri; Yin Tat Lee; Swati Padmanabhan; William Swartworth; David P. Woodruff; Guanghao Ye (STOC, 2024)

On the Emergence of Thinking in LLMs I: Searching for the Right Intuition. - Guanghao Ye; Khiem Duc Pham; Xinzhi Zhang 002; Sivakanth Gopi; Baolin Peng; Beibin Li; Janardhan Kulkarni; Huseyin A. Inan (CoRR, 2025)