Honghao Lin

According to our database, Honghao Lin authored at least 23 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2026

Adversarial Robustness on Insertion-Deletion Streams.

Elena Gribelyuk, Honghao Lin, David P. Woodruff, Huacheng Yu, Samson Zhou

CoRR, February, 2026

L_p Sampling in Distributed Data Streams with Applications to Adversarial Robustness.

Honghao Lin, Zhao Song, David P. Woodruff, Shenghao Xie, Samson Zhou

Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms, 2026

2025

Unbiased Insights: Optimal Streaming Algorithms for ℓ_p Sampling, the Forget Model, and Beyond.

Honghao Lin, Hoai-An Nguyen, William Swartworth, David P. Woodruff

CoRR, August, 2025

On Sketching Trimmed Statistics.

Honghao Lin, Hoai-An Nguyen, David P. Woodruff

CoRR, June, 2025

Lifting Linear Sketches: Optimal Bounds and Adversarial Robustness.

Elena Gribelyuk, Honghao Lin, David P. Woodruff, Huacheng Yu, Samson Zhou

Proceedings of the 57th Annual ACM Symposium on Theory of Computing, 2025

Space Complexity of Minimum Cut Problems in Single-Pass Streams.

Matthew Ding, Alexandro Garces, Jason Li, Honghao Lin, Jelani Nelson, Vihan Shah, David P. Woodruff

Proceedings of the 16th Innovations in Theoretical Computer Science Conference, 2025

2024

Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut.

Yu Cheng, Max Li, Honghao Lin, Zi-Yi Tai, David P. Woodruff, Jason Zhang

Proc. ACM Manag. Data, 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

A Strong Separation for Adversarially Robust ℓ₀ Estimation for Linear Sketches.

Elena Gribelyuk, Honghao Lin, David P. Woodruff, Huacheng Yu, Samson Zhou

CoRR, 2024

Even Sparser Graph Transformers.

Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David P. Woodruff, Danica J. Sutherland

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms.

Yi Li, Honghao Lin, David P. Woodruff

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Strong Separation for Adversarially Robust ℓ0 Estimation for Linear Sketches.

Elena Gribelyuk, Honghao Lin, David P. Woodruff, Huacheng Yu, Samson Zhou

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

2023

The ℓ_<i>p</i>-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines.

Yi Li, Honghao Lin, David P. Woodruff

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Learning the Positions in CountSketch.

Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David P. Woodruff

Proceedings of the Eleventh International Conference on Learning Representations, 2023

ℓ_p-Regression in the Arbitrary Partition Model of Communication.

Yi Li, Honghao Lin, David P. Woodruff

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

The 𝓁_p-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines.

Yi Li, Honghao Lin, David P. Woodruff

CoRR, 2022

Learning Augmented Binary Search Trees.

Honghao Lin, Tian Luo, David P. Woodruff

Proceedings of the International Conference on Machine Learning, 2022

Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra.

Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Streaming Algorithms with Large Approximation Factors.

Yi Li, Honghao Lin, David P. Woodruff, Yuheng Zhang

Proceedings of the Approximation, 2022

2021

Learning-Augmented Sketches for Hessians.

Yi Li, Honghao Lin, David P. Woodruff

CoRR, 2021

Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time.

Yu Cheng, Honghao Lin

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Learning-Augmented Data Stream Algorithms.

Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff

Proceedings of the 8th International Conference on Learning Representations, 2020