Learning-Augmented Data Stream Algorithms. - Tanqiu Jiang; Yi Li 002; Honghao Lin; Yisong Ruan; David P. Woodruff (ICLR, 2020)

Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time. - Yu Cheng 002; Honghao Lin (ICLR, 2021)

Learning-Augmented Sketches for Hessians. - Yi Li 002; Honghao Lin; David P. Woodruff (CoRR, 2021)

Streaming Algorithms with Large Approximation Factors. - Yi Li 002; Honghao Lin; David P. Woodruff; Yuheng Zhang (APPROX/RANDOM, 2022)

Triangle and Four Cycle Counting with Predictions in Graph Streams. - Justin Y. Chen; Talya Eden; Piotr Indyk; Honghao Lin; Shyam Narayanan; Ronitt Rubinfeld; Sandeep Silwal; Tal Wagner; David P. Woodruff; Michael Zhang (ICLR, 2022)

Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. - Nadiia Chepurko; Kenneth L. Clarkson; Lior Horesh; Honghao Lin; David P. Woodruff (ICML, 2022)

Learning Augmented Binary Search Trees. - Honghao Lin; Tian Luo; David P. Woodruff (ICML, 2022)

The 𝓁p-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines. - Yi Li 002; Honghao Lin; David P. Woodruff (CoRR, 2022)

ℓp-Regression in the Arbitrary Partition Model of Communication. - Yi Li 002; Honghao Lin; David P. Woodruff (COLT, 2023)

Learning the Positions in CountSketch. - Yi Li 002; Honghao Lin; Simin Liu; Ali Vakilian; David P. Woodruff (ICLR, 2023)

The ℓp-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines. - Yi Li 002; Honghao Lin; David P. Woodruff (SODA, 2023)

A Strong Separation for Adversarially Robust ℓ0 Estimation for Linear Sketches. - Elena Gribelyuk; Honghao Lin; David P. Woodruff; Huacheng Yu; Samson Zhou (FOCS, 2024)

Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms. - Yi Li 002; Honghao Lin; David P. Woodruff (ICLR, 2024)

Even Sparser Graph Transformers. - Hamed Shirzad; Honghao Lin; Balaji Venkatachalam; Ameya Velingker; David P. Woodruff; Danica J. Sutherland (NeurIPS, 2024)

A Strong Separation for Adversarially Robust ℓ0 Estimation for Linear Sketches. - Elena Gribelyuk; Honghao Lin; David P. Woodruff; Huacheng Yu; Samson Zhou (CoRR, 2024)

A Theory for Compressibility of Graph Transformers for Transductive Learning. - Hamed Shirzad; Honghao Lin; Ameya Velingker; Balaji Venkatachalam; David P. Woodruff; Danica J. Sutherland (CoRR, 2024)

Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut. - Yu Cheng 002; Max Li; Honghao Lin; Zi-Yi Tai; David P. Woodruff; Jason Zhang (Proc. ACM Manag. Data, 2024)

Space Complexity of Minimum Cut Problems in Single-Pass Streams. - Matthew Ding 001; Alexandro Garces; Jason Li 006; Honghao Lin; Jelani Nelson; Vihan Shah; David P. Woodruff (ITCS, 2025)

Lifting Linear Sketches: Optimal Bounds and Adversarial Robustness. - Elena Gribelyuk; Honghao Lin; David P. Woodruff; Huacheng Yu; Samson Zhou (STOC, 2025)

On Sketching Trimmed Statistics. - Honghao Lin; Hoai-An Nguyen; David P. Woodruff (CoRR, 2025)

Unbiased Insights: Optimal Streaming Algorithms for ℓp Sampling, the Forget Model, and Beyond. - Honghao Lin; Hoai-An Nguyen; William Swartworth; David P. Woodruff (CoRR, 2025)