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We give a simplified and improved lower bound for the simplex range reporting problem. We show that given a set $P$ of $n$ points in $\mathbb{R}^d$, any data structure that uses $S(n)$ space to answer such queries must have…

Computational Geometry · Computer Science 2022-10-27 Peyman Afshani , Pingan Cheng

The constrained minimization (respectively maximization) of directed distances and of related generalized entropies is a fundamental task in information theory as well as in the adjacent fields of statistics, machine learning, artificial…

Information Theory · Computer Science 2024-10-28 Michel Broniatowski , Wolfgang Stummer

The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data needed by all known computationally efficient algorithms far…

Computational Complexity · Computer Science 2019-11-19 Matthew Brennan , Guy Bresler , Wasim Huleihel

We prove a theorem concerning the approximation of multivariate functions by deep ReLU networks. We present new error estimates for which the curse of the dimensionality is lessened by establishing a connection with sparse grids.

Numerical Analysis · Mathematics 2018-08-15 Hadrien Montanelli , Qiang Du

Denoising diffusions are a powerful method to generate approximate samples from high-dimensional data distributions. Recent results provide polynomial bounds on their convergence rate, assuming $L^2$-accurate scores. Until now, the tightest…

Machine Learning · Statistics 2024-03-07 Joe Benton , Valentin De Bortoli , Arnaud Doucet , George Deligiannidis

The question of what can be computed, and how efficiently, are at the core of computer science. Not surprisingly, in distributed systems and networking research, an equally fundamental question is what can be computed in a…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-04-01 Fabian Kuhn , Thomas Moscibroda , Roger Wattenhofer

In the LOCAL model, low-diameter decomposition is a useful tool in designing algorithms, as it allows us to shift from the general graph setting to the low-diameter graph setting, where brute-force information gathering can be done…

Data Structures and Algorithms · Computer Science 2026-03-25 Yi-Jun Chang

This paper addresses the issue of design of low-rate sparse-graph codes with linear minimum distance in the blocklength. First, we define a necessary condition which needs to be satisfied when the linear minimum distance is to be ensured.…

Information Theory · Computer Science 2010-10-12 Iryna Andriyanova , Jean-Pierre Tillich

We obtain improved lower bounds for additive spanners, additive emulators, and diameter-reducing shortcut sets. Spanners and emulators are sparse graphs that approximately preserve the distances of a given graph. A shortcut set is a set of…

Data Structures and Algorithms · Computer Science 2023-09-27 Kevin Lu , Virginia Vassilevska Williams , Nicole Wein , Zixuan Xu

For an input graph $G$, an additive spanner is a sparse subgraph $H$ whose shortest paths match those of $G$ up to small additive error. We prove two new lower bounds in the area of additive spanners: 1) We construct $n$-node graphs $G$ for…

Data Structures and Algorithms · Computer Science 2022-10-07 Greg Bodwin , Gary Hoppenworth

Learning sketching matrices for fast and accurate low-rank approximation (LRA) has gained increasing attention. Recently, Bartlett, Indyk, and Wagner (COLT 2022) presented a generalization bound for the learning-based LRA. Specifically, for…

Machine Learning · Computer Science 2022-10-14 Shinsaku Sakaue , Taihei Oki

A prominent tool in many problems involving metric spaces is a notion of randomized low-diameter decomposition. Loosely speaking, $\beta$-decomposition refers to a probability distribution over partitions of the metric into sets of low…

Data Structures and Algorithms · Computer Science 2016-09-29 Lior Kamma , Robert Krauthgamer

Resolving an open question from 2006, we prove the existence of light-weight bounded-degree spanners for unit ball graphs in the metrics of bounded doubling dimension, and we design a simple $\mathcal{O}(\log^*n)$-round distributed…

Computational Geometry · Computer Science 2022-11-08 David Eppstein , Hadi Khodabandeh

We prove three new lower bounds for graph connectivity in the $1$-bit broadcast congested clique model, BCC$(1)$. First, in the KT-$0$ version of BCC$(1)$, in which nodes are aware of neighbors only through port numbers, we show an…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-05-23 Shreyas Pai , Sriram V. Pemmaraju

Sublinear time algorithms for approximating maximum matching size have long been studied. Much of the progress over the last two decades on this problem has been on the algorithmic side. For instance, an algorithm of Behnezhad [FOCS'21]…

Data Structures and Algorithms · Computer Science 2022-11-30 Soheil Behnezhad , Mohammad Roghani , Aviad Rubinstein

We give near-tight lower bounds for the sparsity required in several dimensionality reducing linear maps. First, consider the JL lemma which states that for any set of n vectors in R there is a matrix A in R^{m x d} with m = O(eps^{-2}log…

Data Structures and Algorithms · Computer Science 2012-11-07 Jelani Nelson , Huy L. Nguyen

Among various distance functions for graphs, graph and subgraph edit distances (GED and SED respectively) are two of the most popular and expressive measures. Unfortunately, exact computations for both are NP-hard. To overcome this…

Machine Learning · Computer Science 2023-04-25 Rishabh Ranjan , Siddharth Grover , Sourav Medya , Venkatesan Chakaravarthy , Yogish Sabharwal , Sayan Ranu

We introduce the Density Formula for (topological) drawings of graphs in the plane or on the sphere, which relates the number of edges, vertices, crossings, and sizes of cells in the drawing. We demonstrate its capability by providing…

Random geometric networks consist of 1) a set of nodes embedded randomly in a bounded domain $\mathcal{V} \subseteq \mathbb{R}^d$ and 2) links formed probabilistically according to a function of mutual Euclidean separation. We quantify how…

Social and Information Networks · Computer Science 2016-11-17 Alexander P. Kartun-Giles , Orestis Georgiou , Carl P. Dettmann

We prove strengthened lower bounds for constant-depth set-multilinear formulas. More precisely, we show that over any field, there is an explicit polynomial $f$ in VNP defined over $n^2$ variables, and of degree $n$, such that any…

Computational Complexity · Computer Science 2022-05-03 Deepanshu Kush , Shubhangi Saraf