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Related papers: Consistency of weighted majority votes

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Weighted Majority Voting (WMV) is a well-known optimal decision rule for collective decision making, given the probability of sources to provide accurate information (trustworthiness). However, in reality, the trustworthiness is not a known…

Artificial Intelligence · Computer Science 2024-07-02 Shaojie Bai , Dongxia Wang , Tim Muller , Peng Cheng , Jiming Chen

Majority voting is one of the few black-box interventions that can improve a fixed stochastic predictor: repeated access can be cheaper than changing a high-capability model. Classical fixed-competence theory makes this intervention look…

Machine Learning · Computer Science 2026-05-08 Yi Liu

In this paper, we study the classical problem of estimating the proportion of a finite population. First, we consider a fixed sample size method and derive an explicit sample size formula which ensures a mixed criterion of absolute and…

Statistics Theory · Mathematics 2009-02-03 Xinjia Chen

We study weighted Tikhonov regularization for large-scale linear discrete ill-posed problems with random noise. Under a polynomial upper-bound assumption on the generalized eigenvalues of the discrete forward operator, we derive stochastic…

Numerical Analysis · Mathematics 2026-05-19 Duan-Peng Ling , Wenlong Zhang

The paper considers the problem of multi-objective decision support when outcomes are uncertain. We extend the concept of Pareto-efficient decisions to take into account the uncertainty of decision outcomes across varying contexts. This…

Machine Learning · Statistics 2021-10-20 Sofia Ek , Dave Zachariah , Petre Stoica

To obtain reliable results of expertise, which usually use individual and group expert pairwise comparisons, it is important to summarize (aggregate) expert estimates provided that they are sufficiently consistent. There are several ways to…

Methodology · Statistics 2024-10-07 Vitaliy Tsyganok , Andriy Olenko , Pavlo Roik , Oksana Vlasenko

This paper is an axiomatic study of consistent approval-based multi-winner rules, i.e., voting rules that select a fixed-size group of candidates based on approval ballots. We introduce the class of counting rules and provide an axiomatic…

Computer Science and Game Theory · Computer Science 2019-11-13 Martin Lackner , Piotr Skowron

We study the choice of the regularisation parameter for linear ill-posed problems in the presence of noise that is possibly unbounded but only finite in a weaker norm, and when the noise-level is unknown. For this task, we analyse several…

Numerical Analysis · Mathematics 2021-04-14 Stefan Kindermann , Kemal Raik

In this paper we study several monotonicity axioms in approval-based multi-winner voting rules. We consider monotonicity with respect to the support received by the winners and also monotonicity in the size of the committee. Monotonicity…

Computer Science and Game Theory · Computer Science 2019-02-25 Luis Sánchez-Fernández , Jesús A. Fisteus

Near the entire university faculty directors must select some qualified professors for respected courses in each academic semester. In this sense, factors such as teaching experience, academic training, competition, etc. are considered.…

Artificial Intelligence · Computer Science 2022-08-10 Sana Karimian

This paper presents a new approach based on optimization model to determine the weights of experts in the multi-attribute group decision. Firstly, by minimizing the sum of differences between individual evaluations and the overall…

Optimization and Control · Mathematics 2023-11-22 Yuetong Liu , Chaolang Hu , Shiquan Zhang , Qixiao Hu

Bayesian nonparametric regression under a rescaled Gaussian process prior offers smoothness-adaptive function estimation with near minimax-optimal error rates. Hierarchical extensions of this approach, equipped with stochastic variable…

Statistics Theory · Mathematics 2020-12-15 Sheng Jiang , Surya T. Tokdar

Maximum likelihood estimation furnishes powerful insights into voting theory, and the design of voting rules. However the MLE can usually be badly corrupted by a single outlying sample. This means that a single voter or a group of colluding…

Data Structures and Algorithms · Computer Science 2022-07-19 Allen Liu , Ankur Moitra

In the Byzantine agreement problem, n nodes with possibly different input values aim to reach agreement on a common value in the presence of t < n/3 Byzantine nodes which represent arbitrary failures in the system. This paper introduces a…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-03-08 Darya Melnyk , Yuyi Wang , Roger Wattenhofer

We show how well known rules of back propagation arise from a weighted combination of finite automata. By redefining a finite automata as a predictor we combine the set of all $k$-state finite automata using a weighted majority algorithm.…

Machine Learning · Computer Science 2018-03-30 Finn Macleod

When randomized ensembles such as bagging or random forests are used for binary classification, the prediction error of the ensemble tends to decrease and stabilize as the number of classifiers increases. However, the precise relationship…

Probability · Mathematics 2019-05-01 Miles E. Lopes

We study pattern matching problems on two major representations of uncertain sequences used in molecular biology: weighted sequences (also known as position weight matrices, PWM) and profiles (i.e., scoring matrices). In the simple version,…

Data Structures and Algorithms · Computer Science 2016-07-12 Tomasz Kociumaka , Solon P. Pissis , Jakub Radoszewski

The paper considers the problem of finding the number of dominant voters in two-level voting procedures. At the first stage, voting is conducted among local groups of voters, and at the second stage, the results are aggregated to form a…

Discrete Mathematics · Computer Science 2025-06-11 N. I. Shushko , D. V. Lemtyuzhnikova

Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core…

Machine Learning · Computer Science 2022-06-07 Ben Abramowitz , Nicholas Mattei

This paper is concerned with a class of stochastic optimization problems defined on a Banach space with almost sure conic-type constraints. For this class of problems, we investigate the consistency of optimal values and solutions…

Optimization and Control · Mathematics 2026-03-11 Caroline Geiersbach , Johannes Milz
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