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ORCIDSandeep Silwal
Person information
- affiliation: University of Wisconsin-Madison, Department of Computer Science, Madison, WI, USA
- affiliation (former, PhD): Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
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2020 – today
- 2026
[c42]Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Sandeep Silwal:
How fast can you find a good hypothesis? COLT 2026: 1-2- [c41]Prashant Gokhale, Piotr Indyk, Yuhao Liu, Sandeep Silwal, Tony Chang Wang, Haike Xu:
Compact Geometric Representations of Hierarchies. COLT 2026: 2854-2877 - [c40]Omri Ben-Eliezer, Krzysztof Onak, Sandeep Silwal:
Robust Streaming Against Low-Memory Adversaries. ITCS 2026: 16:1-16:23 - [c39]Shaofeng H.-C. Jiang, Robert Krauthgamer, Shay Sapir, Sandeep Silwal, Di Yue:
Dimension Reduction for Clustering: The Curious Case of Discrete Centers. ITCS 2026: 82:1-82:23 - [c38]Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Shyam Narayanan, Sandeep Silwal:
On the Structure of Replicable Hypothesis Testers. SODA 2026: 5771-5823 - [i52]Fangzhou Wu, Sandeep Silwal, Qiuyi Zhang:
Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective. CoRR abs/2601.18999 (2026) - [i51]Anders Aamand, Justin Y. Chen, Sandeep Silwal:
Skirting Additive Error Barriers for Private Turnstile Streams. CoRR abs/2602.10360 (2026) - [i50]Kiarash Banihashem, Jeff Giliberti, Prashant Gokhale, Samira Goudarzi, MohammadTaghi Hajiaghayi, Yuhao Liu, Morteza Monemizadeh, Sandeep Silwal:
Adversarially Robust Approximate Furthest Neighbor. CoRR abs/2605.16618 (2026) - [i49]Fangzhou Wu, Rikhav Shah, Sandeep Silwal, Qiuyi Zhang:
DynMuon: A Dynamic Spectral Shaping View of Muon. CoRR abs/2605.17109 (2026) - [i48]Fangzhou Wu, Sandeep Silwal, Qiuyi Zhang:
Capturing LLM Capabilities via Evidence-Calibrated Query Clustering. CoRR abs/2605.17110 (2026) - [i47]Prashant Gokhale, Piotr Indyk, Yuhao Liu, Sandeep Silwal, Tony Chang Wang, Haike Xu:
Compact Geometric Representations of Hierarchies. CoRR abs/2606.18520 (2026) - [i46]Prashant Gokhale, Mikhail Khodak, Sandeep Silwal:
Dynamic estimation of slowly varying sequences. CoRR abs/2606.23655 (2026) - 2025
- [j3]Dan Kushnir, Sandeep Silwal:
Cluster Tree for Nearest Neighbor Search. Trans. Mach. Learn. Res. 2025 (2025) - [c37]Piotr Indyk, Isabelle Quaye, Ronitt Rubinfeld, Sandeep Silwal:
Optimal and learned algorithms for the online list update problem with Zipfian accesses. ALT 2025: 611-648 - [c36]Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu:
Learning-Augmented Frequent Directions. ICLR 2025 - [c35]Sandeep Silwal, David P. Woodruff, Qiuyi Zhang:
Beyond Worst-Case Dimensionality Reduction for Sparse Vectors. ICLR 2025 - [c34]Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten:
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. ICML 2025 - [c33]Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu:
Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition. ICML 2025 - [c32]Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu:
Improved Approximations for Hard Graph Problems using Predictions. ICML 2025 - [c31]Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu:
Differentially Private Gomory-Hu Trees. NeurIPS 2025 - [c30]Fangzhou Wu, Sandeep Silwal:
Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving. NeurIPS 2025 - [i45]Sandeep Silwal, David P. Woodruff, Qiuyi Zhang:
Beyond Worst-Case Dimensionality Reduction for Sparse Vectors. CoRR abs/2502.19865 (2025) - [i44]Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu:
Learning-Augmented Frequent Directions. CoRR abs/2503.00937 (2025) - [i43]Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu
:
Improved Approximations for Hard Graph Problems using Predictions. CoRR abs/2505.23967 (2025) - [i42]Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten:
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. CoRR abs/2506.00165 (2025) - [i41]Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu:
Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition. CoRR abs/2507.01873 (2025) - [i40]Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Shyam Narayanan, Sandeep Silwal:
On the Structure of Replicable Hypothesis Testers. CoRR abs/2507.02842 (2025) - [i39]Fangzhou Wu, Sandeep Silwal:
Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving. CoRR abs/2509.02718 (2025) - [i38]Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Sandeep Silwal:
Hypothesis Selection: A High Probability Conundrum. CoRR abs/2509.03734 (2025) - [i37]Shaofeng H.-C. Jiang, Robert Krauthgamer, Shay Sapir, Sandeep Silwal, Di Yue:
Dimension Reduction for Clustering: The Curious Case of Discrete Centers. CoRR abs/2509.07444 (2025) - [i36]Rikhav Shah, Sandeep Silwal, Haike Xu:
Even Faster Kernel Matrix Linear Algebra via Density Estimation. CoRR abs/2510.02540 (2025) - [i35]Omri Ben-Eliezer, Krzysztof Onak, Sandeep Silwal:
Robust Streaming Against Low-Memory Adversaries. CoRR abs/2511.01769 (2025) - 2024
- [c29]Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski:
Efficiently Computing Similarities to Private Datasets. ICLR 2024 - [c28]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Haike Xu:
Statistical-Computational Trade-offs for Density Estimation. NeurIPS 2024 - [c27]Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld, Sandeep Silwal:
Optimal Algorithms for Augmented Testing of Discrete Distributions. NeurIPS 2024 - [i34]Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski:
Efficiently Computing Similarities to Private Datasets. CoRR abs/2403.08917 (2024) - [i33]Haike Xu, Sandeep Silwal, Piotr Indyk:
A Bi-metric Framework for Fast Similarity Search. CoRR abs/2406.02891 (2024) - [i32]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) - [i31]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) - [i30]Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld, Sandeep Silwal:
Optimal Algorithms for Augmented Testing of Discrete Distributions. CoRR abs/2412.00974 (2024) - 2023
- [c26]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 - [c25]Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou:
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. ICLR 2023 - [c24]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi Zhang, Samson Zhou:
Robust Algorithms on Adaptive Inputs from Bounded Adversaries. ICLR 2023 - [c23]Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi:
KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. ICLR 2023 - [c22]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. ICML 2023: 1-18 - [c21]Anders Aamand, Justin Y. Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang:
Constant Approximation for Individual Preference Stable Clustering. NeurIPS 2023 - [c20]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal, Ali Vakilian:
Improved Frequency Estimation Algorithms with and without Predictions. NeurIPS 2023 - [c19]Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten:
Near-Linear Time Algorithm for the Chamfer Distance. NeurIPS 2023 - [c18]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. SODA 2023: 4026-4049 - [c17]Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi:
KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. SustaiNLP 2023: 1-31 - [i29]Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal:
Improved Space Bounds for Learning with Experts. CoRR abs/2303.01453 (2023) - [i28]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) - [i27]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) - [i26]Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal:
Data Structures for Density Estimation. CoRR abs/2306.11312 (2023) - [i25]Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten:
A Near-Linear Time Algorithm for the Chamfer Distance. CoRR abs/2307.03043 (2023) - [i24]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) - [i23]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
- [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 2026-07-20 01:24 CEST by the dblp team
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