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2020 – today
- 2023
[c9]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- [c8]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. ICML 2023: 1-18 - [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 - [i11]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal:
Improved Space Bounds for Learning with Experts. CoRR abs/2303.01453 (2023) - [i10]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) - [i9]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. CoRR abs/2306.11312 (2023) - [i8]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) - 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)
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