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Justin Y. Chen
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2020 – today
- 2024
- [c12]Justin Y. Chen, Piotr Indyk, David P. Woodruff:
Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions. ITCS 2024: 32:1-32:22 - [i16]Keyon Vafa, Justin Y. Chen, Jon M. Kleinberg, Sendhil Mullainathan, Ashesh Rambachan:
Evaluating the World Model Implicit in a Generative Model. CoRR abs/2406.03689 (2024) - [i15]Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu:
Differentially Private Gomory-Hu Trees. CoRR abs/2408.01798 (2024) - [i14]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Haike Xu:
Statistical-Computational Trade-offs for Density Estimation. CoRR abs/2410.23087 (2024) - 2023
- [c11]Nicholas Schiefer, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Tal Wagner:
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees. ACDA 2023: 87-97 - [c10]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. ICML 2023: 1-18 - [c9]Anders Aamand, Justin Y. Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang:
Constant Approximation for Individual Preference Stable Clustering. NeurIPS 2023 - [c8]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal, Ali Vakilian:
Improved Frequency Estimation Algorithms with and without Predictions. NeurIPS 2023 - [c7]Justin Y. Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Shyam Narayanan, Jelani Nelson, Yinzhan Xu:
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. SODA 2023: 5040-5067 - [i13]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal:
Improved Space Bounds for Learning with Experts. CoRR abs/2303.01453 (2023) - [i12]Nicholas Schiefer, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Tal Wagner:
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees. CoRR abs/2304.07652 (2023) - [i11]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. CoRR abs/2306.11312 (2023) - [i10]Anders Aamand, Justin Y. Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang:
Constant Approximation for Individual Preference Stable Clustering. CoRR abs/2309.16840 (2023) - [i9]Justin Y. Chen, Piotr Indyk, David P. Woodruff:
Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions. CoRR abs/2311.17868 (2023) - [i8]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal, Ali Vakilian:
Improved Frequency Estimation Algorithms with and without Predictions. CoRR abs/2312.07535 (2023) - 2022
- [c6]Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang:
Triangle and Four Cycle Counting with Predictions in Graph Streams. ICLR 2022 - [c5]Justin Y. Chen, Piotr Indyk, Tal Wagner:
Streaming Algorithms for Support-Aware Histograms. ICML 2022: 3184-3203 - [c4]Justin Y. Chen, Sandeep Silwal, Ali Vakilian
, Fred Zhang:
Faster Fundamental Graph Algorithms via Learned Predictions. ICML 2022: 3583-3602 - [c3]Anders Aamand, Justin Y. Chen, Piotr Indyk:
(Optimal) Online Bipartite Matching with Degree Information. NeurIPS 2022 - [c2]Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner:
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks. NeurIPS 2022 - [i7]Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang:
Triangle and Four Cycle Counting with Predictions in Graph Streams. CoRR abs/2203.09572 (2022) - [i6]Justin Y. Chen, Shyam Narayanan, Yinzhan Xu:
All-Pairs Shortest Path Distances with Differential Privacy: Improved Algorithms for Bounded and Unbounded Weights. CoRR abs/2204.02335 (2022) - [i5]Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang:
Faster Fundamental Graph Algorithms via Learned Predictions. CoRR abs/2204.12055 (2022) - [i4]Justin Y. Chen, Piotr Indyk, Tal Wagner:
Streaming Algorithms for Support-Aware Histograms. CoRR abs/2207.08686 (2022) - [i3]Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner:
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks. CoRR abs/2211.03232 (2022) - 2021
- [i2]Justin Y. Chen, Piotr Indyk:
Online Bipartite Matching with Predicted Degrees. CoRR abs/2110.11439 (2021) - 2020
- [c1]Justin Y. Chen, Gregory Valiant, Paul Valiant:
Worst-Case Analysis for Randomly Collected Data. NeurIPS 2020
2010 – 2019
- 2019
- [i1]Justin Y. Chen, Gregory Valiant, Paul Valiant:
How bad is worst-case data if you know where it comes from? CoRR abs/1911.03605 (2019)
Coauthor Index
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