Zobrazeno 1 - 10
of 70
pro vyhledávání: '"JAYARAM, RAJESH"'
We study algorithms for approximating the spectral density of a symmetric matrix $A$ that is accessed through matrix-vector product queries. By combining a previously studied Chebyshev polynomial moment matching method with a deflation step that appr
Externí odkaz:
http://arxiv.org/abs/2410.21690
Autor:
Azarmehr, Amir, Behnezhad, Soheil, Jayaram, Rajesh, Łącki, Jakub, Mirrokni, Vahab, Zhong, Peilin
We study the minimum spanning tree (MST) problem in the massively parallel computation (MPC) model. Our focus is particularly on the *strictly sublinear* regime of MPC where the space per machine is $O(n^\delta)$. Here $n$ is the number of vertices a
Externí odkaz:
http://arxiv.org/abs/2408.06455
In this paper, we study streaming algorithms that minimize the number of changes made to their internal state (i.e., memory contents). While the design of streaming algorithms typically focuses on minimizing space and update time, these metrics fail
Externí odkaz:
http://arxiv.org/abs/2406.06821
Autor:
Bateni, MohammadHossein, Dhulipala, Laxman, Fletcher, Willem, Gowda, Kishen N, Hershkowitz, D Ellis, Jayaram, Rajesh, Łącki, Jakub
We give an efficient algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering (HAC), which computes a $c$-approximate clustering in roughly $n^{1+O(1/c^2)}$ time. We obtain our result by combining a new Centroid-Linkage HAC algorithm with
Externí odkaz:
http://arxiv.org/abs/2406.05066
Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines. These models produce a single embedding $x \in \mathbb{R}^d$ per data-point, allowing for fast retrieval via highly optimized maximum inner pr
Externí odkaz:
http://arxiv.org/abs/2405.19504
We consider the PageRank problem in the dynamic setting, where the goal is to explicitly maintain an approximate PageRank vector $\pi \in \mathbb{R}^n$ for a graph under a sequence of edge insertions and deletions. Our main result is a complete chara
Externí odkaz:
http://arxiv.org/abs/2404.16267
Autor:
Bateni, MohammadHossein, Dhulipala, Laxman, Gowda, Kishen N, Hershkowitz, D Ellis, Jayaram, Rajesh, Łącki, Jakub
Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel
Externí odkaz:
http://arxiv.org/abs/2404.14730
We give new data-dependent locality sensitive hashing schemes (LSH) for the Earth Mover's Distance ($\mathsf{EMD}$), and as a result, improve the best approximation for nearest neighbor search under $\mathsf{EMD}$ by a quadratic factor. Here, the met
Externí odkaz:
http://arxiv.org/abs/2403.05041
We consider the fundamental problem of decomposing a large-scale approximate nearest neighbor search (ANNS) problem into smaller sub-problems. The goal is to partition the input points into neighborhood-preserving shards, so that the nearest neighbor
Externí odkaz:
http://arxiv.org/abs/2403.01797
Multi-dimensional Scaling (MDS) is a family of methods for embedding an $n$-point metric into low-dimensional Euclidean space. We study the Kamada-Kawai formulation of MDS: given a set of non-negative dissimilarities $\{d_{i,j}\}_{i , j \in [n]}$ ove
Externí odkaz:
http://arxiv.org/abs/2311.17840