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We consider the problem of finding a low discrepancy coloring for sparse set systems where each element lies in at most t sets. We give an efficient algorithm that finds a coloring with discrepancy O((t log n)^{1/2}), matching the best…

Data Structures and Algorithms · Computer Science 2016-09-13 Nikhil Bansal , Daniel Dadush , Shashwat Garg

Neglecting the second order corrections in weak lensing measurements can lead to a few percent uncertainties on cosmic shears, and becomes more important for cluster lensing mass reconstructions. Existing methods which claim to measure the…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-18 Jun Zhang

We study geometric variations of the discriminating code problem. In the \emph{discrete version} of the problem, a finite set of points $P$ and a finite set of objects $S$ are given in $\mathbb{R}^d$. The objective is to choose a subset…

Computational Geometry · Computer Science 2023-06-30 Sanjana Dey , Florent Foucaud , Subhas C Nandy , Arunabha Sen

For two d-dimensional point sets A, B of size up to n, the Chamfer distance from A to B is defined as CH(A,B) = \sum_{a \in A} \min_{b \in B} \|a-b\|. The Chamfer distance is a widely used measure for quantifying dissimilarity between sets…

Computational Geometry · Computer Science 2025-09-04 Ying Feng , Piotr Indyk

Inspired by the boolean discrepancy problem, we study the following optimization problem which we term \textsc{Spherical Discrepancy}: given $m$ unit vectors $v_1, \dots, v_m$, find another unit vector $x$ that minimizes $\max_i \langle x,…

Computational Complexity · Computer Science 2019-11-19 Chris Jones , Matt McPartlon

This paper defines the notion of class discrepancy for families of functions. It shows that low discrepancy classes admit small offline and streaming coresets. We provide general techniques for bounding the class discrepancy of machine…

Machine Learning · Computer Science 2019-06-13 Zohar Karnin , Edo Liberty

Let $\chi(\mathbb{E}^n)$ denote the chromatic number of the Euclidean space $\mathbb{E}^n$, i.e., the smallest number of colors that can be used to color $\mathbb{E}^n$ so that no two points unit distance apart are of the same color. We…

Combinatorics · Mathematics 2025-04-15 Andrii Arman , Andriy V. Bondarenko , Andriy Prymak , Danylo Radchenko

The multi-fold chromatic number of the plane $\chi_m$ is the smallest number of colors $k$, sufficient to color each point of the Euclidean plane in exactly $m$ colors, so that for any pair of points at a unit distance from each other, two…

Combinatorics · Mathematics 2022-06-28 Jaan Parts

We apply the discrepancy method and a chaining approach to give improved bounds on the coreset complexity of a wide class of kernel functions. Our results give randomized polynomial time algorithms to produce coresets of size…

Machine Learning · Computer Science 2023-10-13 Rainie Bozzai , Thomas Rothvoss

We consider the problem of estimating a $d$-dimensional discrete distribution from its samples observed under a $b$-bit communication constraint. In contrast to most previous results that largely focus on the global minimax error, we study…

Information Theory · Computer Science 2021-11-02 Wei-Ning Chen , Peter Kairouz , Ayfer Özgür

We consider non-binary product codes with MDS components and their iterative row-column algebraic decoding on the erasure channel. Both independent and block erasures are considered in this paper. A compact graph representation is…

Information Theory · Computer Science 2016-03-07 Fanny Jardel , Joseph J. Boutros

We study the $L^{\infty}$ discrepancy of point sets generated by determinantal point processes on all compact, connected two-point homogeneous spaces, namely spheres and projective spaces. Using concentration inequalities and variance…

Classical Analysis and ODEs · Mathematics 2026-05-22 Carlos Beltrán , Ujué Etayo , Giacomo Gigante , Pedro R. López-Gómez , Ryan W. Matzke

The star discrepancy $D_N^*(\mathcal{P})$ is a quantitative measure for the irregularity of distribution of a finite point set $\mathcal{P}$ in the multi-dimensional unit cube which is intimately related to the integration error of…

Number Theory · Mathematics 2018-03-22 Mario Neumüller , Friedrich Pillichshammer

The problem of finding \emph{distance} between \emph{pattern} of length $m$ and \emph{text} of length $n$ is a typical way of generalizing pattern matching to incorporate dissimilarity score. For both Hamming and $L_1$ distances only a…

Data Structures and Algorithms · Computer Science 2018-05-04 Przemysław Uznański

We establish new exponential in dimension lower bounds for the Maximum Halfspace Discrepancy problem, which models linear classification. Both are fundamental problems in computational geometry and machine learning in their exact and…

Computational Geometry · Computer Science 2026-03-20 Alexander Munteanu , Simon Omlor , Jeff M. Phillips

Consider a perfect AO system with a very fine wavefront sampling interval and a very small actuator interval. If this AO system senses wavefront at a wavelength, lambda_{WFS}, and does science imaging at another wavelength, lambda_{SCI},…

Astrophysics · Physics 2008-11-26 Tadashi Nakajima

Upcoming wide field surveys will have many overlapping epochs of the same region of sky. The conventional wisdom is that in order to reduce the errors sufficiently for systematics-limited measurements, like weak lensing, we must do…

In Defective Coloring we are given a graph $G = (V, E)$ and two integers $\chi_d, \Delta^*$ and are asked if we can partition $V$ into $\chi_d$ color classes, so that each class induces a graph of maximum degree $\Delta^*$. We investigate…

Data Structures and Algorithms · Computer Science 2018-01-12 Rémy Belmonte , Michael Lampis , Valia Mitsou

Let $H$ be a triple system with maximum degree $d>1$ and let $r>10^7\sqrt{d}\log^{2}d$. Then $H$ has a proper vertex coloring with $r$ colors such that any two color classes differ in size by at most one. The bound on $r$ is sharp in order…

Combinatorics · Mathematics 2010-05-25 Hal Kierstead , Dhruv Mubayi

Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including hypothesis testing, sampler selection, distribution approximation, and variational inference. In each…

Machine Learning · Statistics 2025-03-26 Alessandro Barp , Carl-Johann Simon-Gabriel , Mark Girolami , Lester Mackey