54 citations · 67 across the 8 of their papers we have counts for
10 papers
Sublinear Time Low-Rank Approximation of Hankel Matrices
Michael Kapralov, Cameron Musco, Kshiteej Sheth
Hankel matrices are an important class of highly-structured matrices, arising across computational mathematics, engineering, and theoretical computer science. It is well-known that…
Improved Algorithms for Kernel Matrix-Vector Multiplication Under Sparsity Assumptions
Piotr Indyk, Michael Kapralov, Kshiteej Sheth +1
Motivated by the problem of fast processing of attention matrices, we study fast algorithms for computing matrix-vector products for asymmetric Gaussian Kernel matrices $K\in \math…
Streaming Attention Approximation via Discrepancy Theory
Ekaterina Kochetkova, Kshiteej Sheth, Insu Han +2
Large language models (LLMs) have achieved impressive success, but their high memory requirements present challenges for long-context token generation. In this paper we study the s…
Sublinear Time Low-Rank Approximation of Toeplitz Matrices
Cameron Musco, Kshiteej Sheth
We present a sublinear time algorithm for computing a near optimal low-rank approximation to any positive semidefinite (PSD) Toeplitz matrix , given no…
Toeplitz Low-Rank Approximation with Sublinear Query Complexity
Michael Kapralov, Hannah Lawrence, Mikhail Makarov +2
We present a sublinear query algorithm for outputting a near-optimal low-rank approximation to any positive semidefinite Toeplitz matrix . In particu…
Towards Non-Uniform k-Center with Constant Types of Radii
Xinrui Jia, Lars Rohwedder, Kshiteej Sheth +1
In the Non-Uniform k-Center problem we need to cover a finite metric space using k balls of different radii that can be scaled uniformly. The goal is to minimize the scaling factor…
Fair Colorful k-Center Clustering
Xinrui Jia, Kshiteej Sheth, Ola Svensson
An instance of colorful k-center consists of points in a metric space that are colored red or blue, along with an integer k and a coverage requirement for each color. The goal is t…
Improved Linear Embeddings via Lagrange Duality
Kshiteej Sheth, Dinesh Garg, Anirban Dasgupta
Near isometric orthogonal embeddings to lower dimensions are a fundamental tool in data science and machine learning. In this paper, we present the construction of such embeddings…
Deep-Learnt Classification of Light Curves
Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke +5
Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and ast…
Deep Neural Networks for HDR imaging
Kshiteej Sheth
We propose novel methods of solving two tasks using Convolutional Neural Networks, firstly the task of generating HDR map of a static scene using differently exposed LDR images of…