Justin Y. Chen

According to our database, Justin Y. Chen authored at least 16 papers between 2019 and 2024.

Collaborative distances:

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

2024

Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions.

Justin Y. Chen, Piotr Indyk, David P. Woodruff

Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

2023

Improved Space Bounds for Learning with Experts.

Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal

CoRR, 2023

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds.

Justin Y. Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Shyam Narayanan, Jelani Nelson, Yinzhan Xu

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

Improved Frequency Estimation Algorithms with and without Predictions.

Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal, Ali Vakilian

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Constant Approximation for Individual Preference Stable Clustering.

Anders Aamand, Justin Y. Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Data Structures for Density Estimation.

Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal

Proceedings of the International Conference on Machine Learning, 2023

Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees.

Nicholas Schiefer, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Tal Wagner

Proceedings of the SIAM Conference on Applied and Computational Discrete Algorithms, 2023

2022

All-Pairs Shortest Path Distances with Differential Privacy: Improved Algorithms for Bounded and Unbounded Weights.

Justin Y. Chen, Shyam Narayanan, Yinzhan Xu

CoRR, 2022

Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks.

Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

(Optimal) Online Bipartite Matching with Degree Information.

Anders Aamand, Justin Y. Chen, Piotr Indyk

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Faster Fundamental Graph Algorithms via Learned Predictions.

Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang

Proceedings of the International Conference on Machine Learning, 2022

Streaming Algorithms for Support-Aware Histograms.

Justin Y. Chen, Piotr Indyk, Tal Wagner

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

2021

Online Bipartite Matching with Predicted Degrees.

Justin Y. Chen, Piotr Indyk

CoRR, 2021

2020

Worst-Case Analysis for Randomly Collected Data.

Justin Y. Chen, Gregory Valiant, Paul Valiant

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

How bad is worst-case data if you know where it comes from?

Justin Y. Chen, Gregory Valiant, Paul Valiant

CoRR, 2019