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2020 – today
- 2023
[c17]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. SODA 2023: 4026-4049- [i25]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal:
Improved Space Bounds for Learning with Experts. CoRR abs/2303.01453 (2023) - [i24]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi Zhang, Samson Zhou:
Robust Algorithms on Adaptive Inputs from Bounded Adversaries. CoRR abs/2304.07413 (2023) - [i23]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) - 2022
- [j2]Sandeep Silwal:
A concentration inequality for the facility location problem. Oper. Res. Lett. 50(2): 213-217 (2022) - [c16]Michael Kapralov, Mikhail Makarov, Sandeep Silwal, Christian Sohler, Jakab Tardos:
Motif Cut Sparsifiers. FOCS 2022: 389-398 - [c15]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 - [c14]Jon C. Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Learning-Augmented $k$-means Clustering. ICLR 2022 - [c13]Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang:
Faster Fundamental Graph Algorithms via Learned Predictions. ICML 2022: 3583-3602 - [c12]Eric Price, Sandeep Silwal, Samson Zhou:
Hardness and Algorithms for Robust and Sparse Optimization. ICML 2022: 17926-17944 - [c11]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 - [c10]Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou:
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming. NeurIPS 2022 - [c9]Piotr Indyk, Sandeep Silwal:
Faster Linear Algebra for Distance Matrices. NeurIPS 2022 - [c8]Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou:
The White-Box Adversarial Data Stream Model. PODS 2022: 15-27 - [i22]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) - [i21]Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou:
The White-Box Adversarial Data Stream Model. CoRR abs/2204.09136 (2022) - [i20]Michael Kapralov, Mikhail Makarov, Sandeep Silwal, Christian Sohler, Jakab Tardos:
Motif Cut Sparsifiers. CoRR abs/2204.09951 (2022) - [i19]Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang:
Faster Fundamental Graph Algorithms via Learned Predictions. CoRR abs/2204.12055 (2022) - [i18]Eric Price, Sandeep Silwal, Samson Zhou:
Hardness and Algorithms for Robust and Sparse Optimization. CoRR abs/2206.14354 (2022) - [i17]Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou:
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming. CoRR abs/2209.10614 (2022) - [i16]Piotr Indyk, Sandeep Silwal:
Faster Linear Algebra for Distance Matrices. CoRR abs/2210.15114 (2022) - [i15]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) - [i14]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. CoRR abs/2211.09964 (2022) - [i13]Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou:
Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation. CoRR abs/2212.00642 (2022) - 2021
- [c7]Rikhav Shah, Sandeep Silwal:
Smoothed Analysis of the Condition Number Under Low-Rank Perturbations. APPROX-RANDOM 2021: 40:1-40:21 - [c6]Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner:
Learning-based Support Estimation in Sublinear Time. ICLR 2021 - [c5]Shyam Narayanan, Sandeep Silwal, Piotr Indyk, Or Zamir:
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering. ICML 2021: 7948-7957 - [c4]Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou:
Adversarial Robustness of Streaming Algorithms through Importance Sampling. NeurIPS 2021: 3544-3557 - [c3]Zachary Izzo, Sandeep Silwal, Samson Zhou:
Dimensionality Reduction for Wasserstein Barycenter. NeurIPS 2021: 15582-15594 - [i12]Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner:
Learning-based Support Estimation in Sublinear Time. CoRR abs/2106.08396 (2021) - [i11]Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou:
Adversarial Robustness of Streaming Algorithms through Importance Sampling. CoRR abs/2106.14952 (2021) - [i10]Shyam Narayanan, Sandeep Silwal, Piotr Indyk, Or Zamir:
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering. CoRR abs/2107.01804 (2021) - [i9]Zachary Izzo, Sandeep Silwal, Samson Zhou:
Dimensionality Reduction for Wasserstein Barycenter. CoRR abs/2110.08991 (2021) - [i8]Jon Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Learning-Augmented k-means Clustering. CoRR abs/2110.14094 (2021) - 2020
- [c2]Rogers Epstein, Sandeep Silwal:
Property Testing of LP-Type Problems. ICALP 2020: 98:1-98:18 - [c1]Maryam Aliakbarpour, Sandeep Silwal:
Testing Properties of Multiple Distributions with Few Samples. ITCS 2020: 69:1-69:41 - [i7]Rikhav Shah, Sandeep Silwal:
Smoothed analysis of the condition number under low-rank perturbations. CoRR abs/2009.01986 (2020) - [i6]Sandeep Silwal:
A Concentration Inequality for the Facility Location Problem. CoRR abs/2012.04488 (2020)
2010 – 2019
- 2019
- [j1]Jesse Michel, Sushruth Reddy, Rikhav Shah, Sandeep Silwal, Ramis Movassagh
:
Directed random geometric graphs. J. Complex Networks 7(5): 792-816 (2019) - [i5]Maryam Aliakbarpour, Sandeep Silwal:
Testing Properties of Multiple Distributions with Few Samples. CoRR abs/1911.07324 (2019) - [i4]Rogers Epstein, Sandeep Silwal:
Property Testing of LP-Type Problems. CoRR abs/1911.08320 (2019) - [i3]Rikhav Shah, Sandeep Silwal:
Using Dimensionality Reduction to Optimize t-SNE. CoRR abs/1912.01098 (2019) - 2018
- [i2]Jesse Michel, Sushruth Reddy, Rikhav Shah, Sandeep Silwal, Ramis Movassagh:
Directed Random Geometric Graphs. CoRR abs/1808.02046 (2018) - [i1]Sandeep Silwal, Jonathan Tidor:
Spectral methods for testing cluster structure of graphs. CoRR abs/1812.11564 (2018)
Coauthor Index
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last updated on 2023-05-13 03:57 CEST by the dblp team
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