Search Results for author: Sandeep Silwal

Found 19 papers, 2 papers with code

Efficiently Computing Similarities to Private Datasets

no code implementations13 Mar 2024 Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski

We abstract out this common subroutine and study the following fundamental algorithmic problem: Given a similarity function $f$ and a large high-dimensional private dataset $X \subset \mathbb{R}^d$, output a differentially private (DP) data structure which approximates $\sum_{x \in X} f(x, y)$ for any query $y$.

Density Estimation Dimensionality Reduction

Improved Frequency Estimation Algorithms with and without Predictions

no code implementations NeurIPS 2023 Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal, Ali Vakilian

In particular, their learning-augmented frequency estimation algorithm uses a learned heavy-hitter oracle which predicts which elements will appear many times in the stream.

A Near-Linear Time Algorithm for the Chamfer Distance

no code implementations6 Jul 2023 Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten

For any two point sets $A, B \subset \mathbb{R}^d$ of size up to $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A, B)=\sum_{a \in A} \min_{b \in B} d_X(a, b)$, where $d_X$ is the underlying distance measure (e. g., the Euclidean or Manhattan distance).

Data Structures for Density Estimation

1 code implementation20 Jun 2023 Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal

We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is "close" to $p$.

Density Estimation

Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees

no code implementations15 Apr 2023 Nicholas Schiefer, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Tal Wagner

An $\varepsilon$-approximate quantile sketch over a stream of $n$ inputs approximates the rank of any query point $q$ - that is, the number of input points less than $q$ - up to an additive error of $\varepsilon n$, generally with some probability of at least $1 - 1/\mathrm{poly}(n)$, while consuming $o(n)$ space.

Improved Space Bounds for Learning with Experts

no code implementations2 Mar 2023 Anders Aamand, Justin Y. Chen, Huy Lê Nguyen, Sandeep Silwal

We give improved tradeoffs between space and regret for the online learning with expert advice problem over $T$ days with $n$ experts.

Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation

no code implementations1 Dec 2022 Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou

We build on the recent Kernel Density Estimation framework, which (after preprocessing in time subquadratic in $n$) can return estimates of row/column sums of the kernel matrix.

Density Estimation

Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks

no code implementations6 Nov 2022 Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner

However, those simulations involve neural networks for the 'combine' function of size polynomial or even exponential in the number of graph nodes $n$, as well as feature vectors of length linear in $n$.

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming

no code implementations21 Sep 2022 Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou

We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i. e., consistency.

Hardness and Algorithms for Robust and Sparse Optimization

no code implementations29 Jun 2022 Eric Price, Sandeep Silwal, Samson Zhou

We further show fine-grained hardness of robust regression through a reduction from the minimum-weight $k$-clique conjecture.

regression

Triangle and Four Cycle Counting with Predictions in Graph Streams

no code implementations ICLR 2022 Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang

We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature.

Dimensionality Reduction for Wasserstein Barycenter

no code implementations NeurIPS 2021 Zachary Izzo, Sandeep Silwal, Samson Zhou

In order to cope with this "curse of dimensionality," we study dimensionality reduction techniques for the Wasserstein barycenter problem.

Dimensionality Reduction

Cluster Tree for Nearest Neighbor Search

no code implementations29 Sep 2021 Dan Kushnir, Sandeep Silwal

In addition, we show theoretically and empirically that ClusterTree finds partitions which are superior to those found by RP trees in preserving the cluster structure of the input dataset.

Adversarial Robustness of Streaming Algorithms through Importance Sampling

no code implementations NeurIPS 2021 Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction.

Adversarial Robustness Clustering +1

Learning-based Support Estimation in Sublinear Time

no code implementations ICLR 2021 Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner

We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements.

Using Dimensionality Reduction to Optimize t-SNE

1 code implementation2 Dec 2019 Rikhav Shah, Sandeep Silwal

t-SNE is a popular tool for embedding multi-dimensional datasets into two or three dimensions.

Clustering Dimensionality Reduction

Testing Properties of Multiple Distributions with Few Samples

no code implementations17 Nov 2019 Maryam Aliakbarpour, Sandeep Silwal

We propose a new setting for testing properties of distributions while receiving samples from several distributions, but few samples per distribution.

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