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Min-max optimization of an objective function $f: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}$ is an important model for robustness in an adversarial setting, with applications to many areas including optimization, economics,…

Data Structures and Algorithms · Computer Science 2021-05-05 Oren Mangoubi , Nisheeth K. Vishnoi

We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed…

Machine Learning · Statistics 2018-02-02 Hsiang-Ling Hsu , Yuan-Chin Ivan Chang , Ray-Bing Chen

Sparse signal recovery deals with finding the sparsest solution of an under-determined linear system $\vx = \mQ\vs$. In this paper, we propose a novel greedy approach to addressing the challenges from such a problem. Such an approach is…

Information Theory · Computer Science 2026-04-09 Gang Li , Qiuwei Li , Shuang Li , Wu Angela Li

This paper is devoted to theoretical aspects on optimality of sparse approximation. We undertake a quantitative study of new types of greedy-like bases that have recently arisen in the context of nonlinear $m$-term approximation in Banach…

Functional Analysis · Mathematics 2022-05-20 Fernando Albiac , Jose L. Ansorena , Miguel Berasategui

The asymptotic behaviors of the integrated density of states $N(\lambda)$ of Schr\"odinger operators with nonpositive potentials associated with Gibbs point processes are studied. It is shown that for some Gibbs point processes, the leading…

Probability · Mathematics 2022-10-21 Yuta Nakagawa

The goal of this paper is to study the performance of the Thresholding Greedy Algorithm (TGA) when we increase the size of greedy sums by a constant factor $\lambda\geqslant 1$. We introduce the so-called $\lambda$-almost greedy and…

Functional Analysis · Mathematics 2023-02-13 Hung Viet Chu

We continue our study of the Thresholding Greedy Algorithm when we restrict the vectors involved in our approximations so that they either are supported on intervals of $\mathbb N$ or have constant coefficients. We introduce and…

Functional Analysis · Mathematics 2023-02-14 Miguel Berasategui , Pablo M. Berná , Hung Viet Chu

We consider the $X$-Greedy Algorithm and the Dual Greedy Algorithm in a finite-dimensional Banach space with a strictly monotone basis as the dictionary. We show that when the dictionary is an initial segment of the Haar basis in $L_p[0,1]$…

Functional Analysis · Mathematics 2015-05-13 S. J. Dilworth , E. Odell , Th. Schlumprecht , A. Zsak

Many image processing applications benefited remarkably from the theory of sparsity. One model of sparsity is the cosparse analysis one. It was shown that using l_1-minimization one might stably recover a cosparse signal from a small set of…

Numerical Analysis · Mathematics 2015-02-10 Raja Giryes

We shall present new characterizations of partially greedy and almost greedy bases. A new class of basis (which we call reverse partially greedy basis) arises naturally from these characterizations of partially greedy bases. We also give…

Functional Analysis · Mathematics 2018-05-18 S. J. Dilworth , Divya Khurana

We consider the following interacting particle system: There is a ``gas'' of particles, each of which performs a continuous time simple random walk on the d-dimensional lattice. These particles are called A-particles and move independently…

Probability · Mathematics 2007-05-23 Harry Kesten , Vladas Sidoravicius

Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using group information have been well-studied by shrinkage-based…

Machine Learning · Statistics 2018-09-28 Wei Qian , Wending Li , Yasuhiro Sogawa , Ryohei Fujimaki , Xitong Yang , Ji Liu

A deterministic approximation algorithm is presented for the maximization of non-monotone submodular functions over a ground set of size $n$ subject to cardinality constraint $k$; the algorithm is based upon the idea of interlacing two…

Data Structures and Algorithms · Computer Science 2019-10-28 Alan Kuhnle

We consider the problem of optimally utilizing $N$ resources, each in an unknown binary state. The state of each resource can be inferred from state-dependent noisy measurements. Depending on its state, utilizing a resource results in…

Systems and Control · Computer Science 2017-05-18 Lorenzo Ferrari , Qing Zhao , Anna Scaglione

A lot of recent activity in the theory of cluster algebras has been directed towards various constructions of "natural" bases in them. One of the approaches to this problem was developed several years ago by P.Sherman - A.Zelevinsky who…

Rings and Algebras · Mathematics 2019-11-01 Kyungyong Lee , Li Li , Andrei Zelevinsky

Envy-freeness and Pareto Efficiency are two major goals in welfare economics. The existence of an allocation that satisfies both conditions has been studied for a long time. Whether items are indivisible or divisible, it is impossible to…

Computer Science and Game Theory · Computer Science 2019-11-11 Richard Cole , Yixin Tao

We study sparse approximate solutions to convex optimization problems. It is known that in many engineering applications researchers are interested in an approximate solution of an optimization problem as a linear combination of elements…

Machine Learning · Statistics 2012-06-05 V. N. Temlyakov

In recent work, Jon Kleinberg considered a small-world network model consisting of a d-dimensional lattice augmented with shortcuts. The probability of a shortcut being present between two points decays as a power of the distance between…

Probability · Mathematics 2007-05-23 M. Draief , A. Ganesh

This paper introduces a new asymptotic regime for simplifying stochastic models having non-stationary effects, such as those that arise in the presence of time-of-day effects. This regime describes an operating environment within which the…

Probability · Mathematics 2018-07-19 Zeyu Zheng , Harsha Honnappa , Peter W. Glynn

We consider an exploration algorithm where at each step, a random number of items become active while related items get explored. Given an initial number of items $N$ growing to infinity and building on a strong homogeneity assumption, we…

Probability · Mathematics 2015-04-10 Paola Bermolen , Matthieu Jonckheere , Jaron Sanders