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The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity. To remedy this, conventional approaches such as re-ranking improve diversity by…

Machine Learning · Computer Science 2023-06-12 Itay Eilat , Nir Rosenfeld

This paper addresses the problem of sequential submodular maximization: selecting and ranking items in a sequence to optimize some composite submodular function. In contrast to most of the previous works, which assume access to the utility…

Machine Learning · Computer Science 2024-09-10 Jing Yuan , Shaojie Tang

Online social systems have become important platforms for viral marketing where the advertising of products is carried out with the communication of users. After adopting the product, the seed buyers may spread the information to their…

Social and Information Networks · Computer Science 2018-12-14 Guangmo Tong , Weili Wu , Ding-Zhu Du

This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices. We first benefit from a convex optimization which develops $l_1$-norm penalty to encourage the sparsity and…

Statistics Theory · Mathematics 2014-08-08 Shenglong Zhou , Naihua Xiu , Ziyan Luo , Lingchen Kong

We consider symmetric (not complete intersection) numerical semigroups S_6, generated by a set of six positive integers {d_1,...,d_6}, gcd(d_1,...,d_6)=1, and derive inequalities for degrees of syzygies of such semigroups and find the lower…

Commutative Algebra · Mathematics 2018-08-29 Leonid G. Fel

While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we…

Methodology · Statistics 2023-09-06 Yifan Cui , Eric Tchetgen Tchetgen

In this paper we show that the negative sample distance covariance function is a quasi-concave set function of samples of random variables that are not statistically independent. We use these properties to propose greedy algorithms to…

Methodology · Statistics 2017-02-20 Praneeth Vepakomma , Yulia Kempner

We consider the problem of minimizing a sum of $n$ functions over a convex parameter set $\mathcal{C} \subset \mathbb{R}^p$ where $n\gg p\gg 1$. In this regime, algorithms which utilize sub-sampling techniques are known to be effective. In…

Machine Learning · Statistics 2015-12-03 Murat A. Erdogdu , Andrea Montanari

We study the discrete bin covering problem where a multiset of items from a fixed set $S \subseteq (0,1]$ must be split into disjoint subsets while maximizing the number of subsets whose contents sum to at least $1$. We study the online…

Data Structures and Algorithms · Computer Science 2024-01-29 Magnus Berg , Shahin Kamali

The problem of designing a profit-maximizing, Bayesian incentive compatible and individually rational mechanism with flexible consumers and costly heterogeneous supply is considered. In our setup, each consumer is associated with a…

Computer Science and Game Theory · Computer Science 2018-02-01 Shiva Navabi , Ashutosh Nayyar

We describe general methods for enumerating subsemigroups of finite semigroups and techniques to improve the algorithmic efficiency of the calculations. As a particular application we use our algorithms to enumerate all transformation…

Group Theory · Mathematics 2017-03-02 James East , Attila Egri-Nagy , James D. Mitchell

We introduce a new approach for the numerical pricing of American options. The main idea is to choose a finite number of suitable excessive functions (randomly) and to find the smallest majorant of the gain function in the span of these…

Computational Finance · Quantitative Finance 2013-10-17 Sören Christensen

Semigroup theory is a branch of abstract algebra, and it provides mathematical tools for the theory of computation. Finite semigroups can describe state transition systems and thus they model physically realizable computers. Engineering…

Group Theory · Mathematics 2026-01-22 James East , Attila Egri-Nagy , Andrew R. Francis , James D. Mitchell

In this paper we investigate the computational complexity of motivating time-inconsistent agents to complete long term projects. We resort to an elegant graph-theoretic model, introduced by Kleinberg and Oren, which consists of a task graph…

Computational Complexity · Computer Science 2016-01-05 Susanne Albers , Dennis Kraft

Importance sampling algorithms are discussed in detail, with an emphasis on implicit sampling, and applied to data assimilation via particle filters. Implicit sampling makes it possible to use the data to find high-probability samples at…

Computation · Statistics 2015-06-02 Alexandre J. Chorin , Fei Lu , Robert N. Miller , Matthias Morzfeld , Xuemin Tu

Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large…

Machine Learning · Computer Science 2023-03-10 Jing Yuan , Shaojie Tang

In this paper we present and study the numerical duplication of a numerical semigroup, a construction that, starting with a numerical semigroup $S$ and a semigroup ideal $E\subseteq S$, produces a new numerical semigroup, denoted by…

Commutative Algebra · Mathematics 2012-11-16 Marco D'Anna , Francesco Strazzanti

A natural operation on numerical semigroups is taking a quotient by a positive integer. If $\mathcal S$ is a quotient of a numerical semigroup with $k$ generators, we call $\mathcal S$ a $k$-quotient. We give a necessary condition for a…

Commutative Algebra · Mathematics 2022-12-20 Tristram Bogart , Christopher O'Neill , Kevin Woods

In this paper we present an alternative approach to symbolic segmentation; instead of implementing a new method we approach symbolic segmentation as an algorithm selection problem. That is, let there be $n$ available algorithms for symbolic…

Computer Vision and Pattern Recognition · Computer Science 2015-06-01 Martin Lukac , Kamila Abdiyeva , Michitaka Kameyama

Evaluating large language models across many benchmarks is expensive, yet many benchmarks are highly correlated. We formalize the selection of a small, informative subset as submodular maximization under a multivariate Gaussian model.…

Artificial Intelligence · Computer Science 2026-05-05 Alexander Smola