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We consider the computation of an approximately stationary point for a Lipschitz and semialgebraic function $f$ with a local oracle. If $f$ is smooth, simple deterministic methods have dimension-free finite oracle complexities. For the…

Optimization and Control · Mathematics 2022-10-14 Lai Tian , Anthony Man-Cho So

This work proposes ensemble Kalman randomized maximum likelihood estimation, a new derivative-free method for performing randomized maximum likelihood estimation, which is a method that can be used to generate approximate samples from…

Numerical Analysis · Mathematics 2025-07-08 Pavlos Stavrinides , Elizabeth Qian

The surprising results of Karp, Vazirani and Vazirani and (respectively) Buchbinder et al are examples where rather simple randomizations provide provably better approximations than the corresponding deterministic counterparts for online…

Data Structures and Algorithms · Computer Science 2016-08-11 Nicolas Pena , Allan Borodin

We design fast algorithms for repeatedly sampling from strongly Rayleigh distributions, which include random spanning tree distributions and determinantal point processes. For a graph $G=(V, E)$, we show how to approximately sample…

Data Structures and Algorithms · Computer Science 2022-09-20 Nima Anari , Yang P. Liu , Thuy-Duong Vuong

This document is an expanded version of a lecture presented at a conference on "Thin Groups and Superstrong Approximation" held at the Mathematical Sciences Research Institute in February 2012. Superstrong approximation is a criterion on a…

Number Theory · Mathematics 2013-03-12 Jordan S. Ellenberg

Designing distributed and scalable algorithms to improve network connectivity is a central topic in peer-to-peer networks. In this paper we focus on the following well-known problem: given an $n$-node $d$-regular network for $d=\Omega(\log…

Data Structures and Algorithms · Computer Science 2015-10-28 Zeyuan Allen-Zhu , Aditya Bhaskara , Silvio Lattanzi , Vahab Mirrokni , Lorenzo Orecchia

In the setting of high-dimensional linear models with Gaussian noise, we investigate the possibility of confidence statements connected to model selection. Although there exist numerous procedures for adaptive point estimation, the…

Statistics Theory · Mathematics 2009-10-07 Angelika Rohde , Lutz Duembgen

Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this…

Computation · Statistics 2026-02-09 Grégoire Clarté , Christian P. Robert , Robin Ryder , Julien Stoehr

We study the problem of modeling a binary operation that satisfies some algebraic requirements. We first construct a neural network architecture for Abelian group operations and derive a universal approximation property. Then, we extend it…

Machine Learning · Computer Science 2021-02-25 Kenshin Abe , Takanori Maehara , Issei Sato

This work establishes a new probabilistic bound on the number of elements to generate finite nilpotent groups. Let $\varphi_k(G)$ denote the probability that $k$ random elements generate a finite nilpotent group $G$. For any $0 < \epsilon <…

Quantum Physics · Physics 2025-11-26 Ziyuan Dong , Xiang Fan , Tengxun Zhong , Daowen Qiu

We develop a theory of higher order structures in compact abelian groups. In the frame of this theory we prove general inverse theorems and regularity lemmas for Gowers's uniformity norms. We put forward an algebraic interpretation of the…

Combinatorics · Mathematics 2012-03-13 Balazs Szegedy

We present a new deterministic algorithm for distributed weighted all pairs shortest paths (APSP) in both undirected and directed graphs. Our algorithm runs in $\tilde{O}(n^{4/3})$ rounds in the Congest models on graphs with arbitrary edge…

Data Structures and Algorithms · Computer Science 2020-05-20 Udit Agarwal , Vijaya Ramachandran

We improve recently published results about resources of Restricted Boltzmann Machines (RBM) and Deep Belief Networks (DBN) required to make them Universal Approximators. We show that any distribution p on the set of binary vectors of…

Machine Learning · Statistics 2010-07-27 Guido Montufar , Nihat Ay

In this work, we study the classic submodular maximization problem under knapsack constraints and beyond. We first present an $(7/16-\varepsilon)$-approximate algorithm for single knapsack constraint, which requires…

Data Structures and Algorithms · Computer Science 2020-12-22 Wenxin Li

For nearly a century, mathematicians have been developing techniques for constructing abelian automorphism groups of combinatorial objects, and, conversely, constructing combinatorial objects from abelian groups. While abelian groups are a…

Combinatorics · Mathematics 2024-07-29 Eric Swartz , James A. Davis , John Polhill , Ken W. Smith

Capacities on a finite set are sets functions vanishing on the empty set and being monotonic w.r.t. inclusion. Since the set of capacities is an order polytope, the problem of randomly generating capacities amounts to generating all linear…

Discrete Mathematics · Computer Science 2022-06-13 Michel Grabisch , Christophe Labreuche , Peiqi Sun

If we pick two elements of a non-abelian group at random, the odds this pair commutes is at most 5/8, so there is a "gap" between abelian and non-abelian groups \cite{G}. We prove a "topological" generalization estimating the odds a word…

Group Theory · Mathematics 2012-05-29 John Mangual

We continue classification of finite groups which can be used as symmetry group of the scalar sector of the four-Higgs-doublet model (4HDM). Our objective is to systematically construct non-abelian groups via the group extension procedure,…

High Energy Physics - Phenomenology · Physics 2024-09-11 Jiazhen Shao , Igor P. Ivanov , Mikko Korhonen

We give an explicit (in particular, deterministic polynomial time) construction of subspaces X of R^N of dimension (1-o(1))N such that for every element x in X, |x|_1 and N^{1/2} |x|_2 are equivalent up to a factor of (log N)^{log log log…

Metric Geometry · Mathematics 2009-03-26 Venkatesan Guruswami , James R. Lee , Alexander Razborov

The current definition of a Bayesian credible set cannot, in general, achieve an arbitrarily preassigned credible level. This drawback is particularly acute for classification problems, where there are only a finite number of achievable…

Statistics Theory · Mathematics 2023-08-23 Chaegeun Song , Bing Li
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