Spectral methods for testing cluster structure of graphs. - Sandeep Silwal; Jonathan Tidor (CoRR, 2018)

Directed random geometric graphs. - Jesse Michel; Sushruth Reddy; Rikhav Shah; Sandeep Silwal; Ramis Movassagh (J. Complex Networks, 2019)

Using Dimensionality Reduction to Optimize t-SNE. - Rikhav Shah; Sandeep Silwal (CoRR, 2019)

Property Testing of LP-Type Problems. - Rogers Epstein; Sandeep Silwal (ICALP, 2020)

Testing Properties of Multiple Distributions with Few Samples. - Maryam Aliakbarpour; Sandeep Silwal (ITCS, 2020)

Smoothed Analysis of the Condition Number Under Low-Rank Perturbations. - Rikhav Shah; Sandeep Silwal (APPROX-RANDOM, 2021)

Learning-based Support Estimation in Sublinear Time. - Talya Eden; Piotr Indyk; Shyam Narayanan; Ronitt Rubinfeld; Sandeep Silwal; Tal Wagner (ICLR, 2021)

Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering. - Shyam Narayanan; Sandeep Silwal; Piotr Indyk; Or Zamir (ICML, 2021)

Adversarial Robustness of Streaming Algorithms through Importance Sampling. - Vladimir Braverman; Avinatan Hassidim; Yossi Matias; Mariano Schain; Sandeep Silwal; Samson Zhou (NeurIPS, 2021)

Dimensionality Reduction for Wasserstein Barycenter. - Zachary Izzo; Sandeep Silwal; Samson Zhou (NeurIPS, 2021)

Motif Cut Sparsifiers. - Michael Kapralov; Mikhail Makarov; Sandeep Silwal; Christian Sohler; Jakab Tardos (FOCS, 2022)

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)

Learning-Augmented $k$-means Clustering. - Jon C. Ergun; Zhili Feng; Sandeep Silwal; David P. Woodruff; Samson Zhou (ICLR, 2022)

Hardness and Algorithms for Robust and Sparse Optimization. - Eric Price 001; Sandeep Silwal; Samson Zhou (ICML, 2022)

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

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming. - Elena Grigorescu; Young-San Lin; Sandeep Silwal; Maoyuan Song; Samson Zhou (NeurIPS, 2022)

Faster Linear Algebra for Distance Matrices. - Piotr Indyk; Sandeep Silwal (NeurIPS, 2022)

The White-Box Adversarial Data Stream Model. - Miklós Ajtai; Vladimir Braverman; T. S. Jayram; Sandeep Silwal; Alec Sun; David P. Woodruff; Samson Zhou (PODS, 2022)

Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (CoRR, 2022)

A concentration inequality for the facility location problem. - Sandeep Silwal (Oper. Res. Lett., 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)

Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (ICLR, 2023)

Robust Algorithms on Adaptive Inputs from Bounded Adversaries. - Yeshwanth Cherapanamjeri; Sandeep Silwal; David P. Woodruff; Fred Zhang; Qiuyi Zhang 001; Samson Zhou (ICLR, 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)

Near-Linear Time Algorithm for the Chamfer Distance. - Ainesh Bakshi; Piotr Indyk; Rajesh Jayaram; Sandeep Silwal; Erik Waingarten (NeurIPS, 2023)

Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. - Yeshwanth Cherapanamjeri; Sandeep Silwal; David P. Woodruff; Samson Zhou (SODA, 2023)

KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. - Sandeep Silwal; Sara Ahmadian; Andrew Nystrom; Andrew McCallum; Deepak Ramachandran; Seyed Mehran Kazemi (SustaiNLP, 2023)

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

Efficiently Computing Similarities to Private Datasets. - Arturs Backurs; Zinan Lin 001; Sepideh Mahabadi; Sandeep Silwal; Jakub Tarnawski (ICLR, 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)

Optimal Algorithms for Augmented Testing of Discrete Distributions. - Maryam Aliakbarpour; Piotr Indyk; Ronitt Rubinfeld; Sandeep Silwal (NeurIPS, 2024)

A Bi-metric Framework for Fast Similarity Search. - Haike Xu; Sandeep Silwal; Piotr Indyk (CoRR, 2024)

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

Optimal and learned algorithms for the online list update problem with Zipfian accesses. - Piotr Indyk; Isabelle Quaye; Ronitt Rubinfeld; Sandeep Silwal (ALT, 2025)

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

Beyond Worst-Case Dimensionality Reduction for Sparse Vectors. - Sandeep Silwal; David P. Woodruff; Qiuyi Zhang 001 (ICLR, 2025)

Cluster Tree for Nearest Neighbor Search. - Dan Kushnir; Sandeep Silwal (Trans. Mach. Learn. Res., 2025)

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

Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. - Jie Gao; Rajesh Jayaram; Benedikt Kolbe; Shay Sapir; Chris Schwiegelshohn; Sandeep Silwal; Erik Waingarten (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)