How bad is worst-case data if you know where it comes from? - Justin Y. Chen; Gregory Valiant; Paul Valiant (CoRR, 2019)

Worst-Case Analysis for Randomly Collected Data. - Justin Y. Chen; Gregory Valiant; Paul Valiant (NeurIPS, 2020)

Online Bipartite Matching with Predicted Degrees. - Justin Y. Chen; Piotr Indyk (CoRR, 2021)

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)

Streaming Algorithms for Support-Aware Histograms. - Justin Y. Chen; Piotr Indyk; Tal Wagner (ICML, 2022)

Faster Fundamental Graph Algorithms via Learned Predictions. - Justin Y. Chen; Sandeep Silwal; Ali Vakilian; Fred Zhang (ICML, 2022)

(Optimal) Online Bipartite Matching with Degree Information. - Anders Aamand; Justin Y. Chen; Piotr Indyk (NeurIPS, 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 (NeurIPS, 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)

Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees. - Nicholas Schiefer; Justin Y. Chen; Piotr Indyk; Shyam Narayanan; Sandeep Silwal; Tal Wagner (ACDA, 2023)

Data Structures for Density Estimation. - Anders Aamand; Alexandr Andoni; Justin Y. Chen; Piotr Indyk; Shyam Narayanan; Sandeep Silwal (ICML, 2023)

Constant Approximation for Individual Preference Stable Clustering. - Anders Aamand; Justin Y. Chen; Allen Liu; Sandeep Silwal; Pattara Sukprasert; Ali Vakilian; Fred Zhang (NeurIPS, 2023)

Improved Frequency Estimation Algorithms with and without Predictions. - Anders Aamand; Justin Y. Chen; Huy Lê Nguyen; Sandeep Silwal; Ali Vakilian (NeurIPS, 2023)

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. - Justin Y. Chen; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Shyam Narayanan; Jelani Nelson; Yinzhan Xu (SODA, 2023)

Improved Space Bounds for Learning with Experts. - Anders Aamand; Justin Y. Chen; Huy Lê Nguyen; Sandeep Silwal (CoRR, 2023)

Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions. - Justin Y. Chen; Piotr Indyk; David P. Woodruff (ITCS, 2024)

Statistical-Computational Trade-offs for Density Estimation. - Anders Aamand; Alexandr Andoni; Justin Y. Chen; Piotr Indyk; Shyam Narayanan; Sandeep Silwal; Haike Xu (NeurIPS, 2024)

Evaluating the World Model Implicit in a Generative Model. - Keyon Vafa; Justin Y. Chen; Ashesh Rambachan; Jon M. Kleinberg; Sendhil Mullainathan (NeurIPS, 2024)

Differentially Private Gomory-Hu Trees. - Anders Aamand; Justin Y. Chen; Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka; Sandeep Silwal; Yinzhan Xu (CoRR, 2024)

Learning-Augmented Frequent Directions. - Anders Aamand; Justin Y. Chen; Siddharth Gollapudi; Sandeep Silwal; Hao Wu (ICLR, 2025)

Scalable Private Partition Selection via Adaptive Weighting. - Justin Y. Chen; Vincent Cohen-Addad; Alessandro Epasto; Morteza Zadimoghaddam (CoRR, 2025)

Private Text Generation by Seeding Large Language Model Prompts. - Supriya Nagesh; Justin Y. Chen; Nina Mishra; Tal Wagner (CoRR, 2025)

Improved Approximations for Hard Graph Problems using Predictions. - Anders Aamand; Justin Y. Chen; Siddharth Gollapudi; Sandeep Silwal; Hao Wu (CoRR, 2025)

Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition. - Anders Aamand; Justin Y. Chen; Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka; Sandeep Silwal; Yinzhan Xu (CoRR, 2025)

On the Structure of Replicable Hypothesis Testers. - Anders Aamand; Maryam Aliakbarpour; Justin Y. Chen; Shyam Narayanan; Sandeep Silwal (CoRR, 2025)