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Related papers: A Linear Theory of Multi-Winner Voting

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In this paper, we propose LEURN: a neural network architecture that learns univariate decision rules. LEURN is a white-box algorithm that results into univariate trees and makes explainable decisions in every stage. In each layer, LEURN…

Machine Learning · Computer Science 2023-03-28 Caglar Aytekin

Reinforcement Learning from AI Feedback (RLAIF) relies on LLM judges as preference measurement instruments, yet these instruments are fundamentally limited by random measurement errors -- stochastic fluctuations that manifest as preference…

Artificial Intelligence · Computer Science 2026-05-26 Boyin Liu , Zhuo Zhang , Sen Huang , Lipeng Xie , Qingxu Fu , Haoran Chen , LI YU , Tianyi Hu , Zhaoyang Liu , Bolin Ding , Dongbin Zhao

The PAC-Bayesian framework has significantly advanced the understanding of statistical learning, particularly for majority voting methods. Despite its successes, its application to multi-view learning -- a setting with multiple…

Machine Learning · Computer Science 2025-10-15 Mehdi Hennequin , Abdelkrim Zitouni , Khalid Benabdeslem , Haytham Elghazel , Yacine Gaci

Several recent works have developed a new, probabilistic interpretation for numerical algorithms solving linear systems in which the solution is inferred in a Bayesian framework, either directly or by inferring the unknown action of the…

Computation · Statistics 2018-10-18 Simon Bartels , Jon Cockayne , Ilse C. F. Ipsen , Philipp Hennig

This paper shows that sequential statistical analysis techniques can be generalised to the problem of selecting between alternative forecasting methods using scoring rules. A return to basic principles is necessary in order to show that…

Statistics Theory · Mathematics 2025-05-15 David T. Frazier , Donald S. Poskitt

We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable for multi-class problems when the data is noisy, scarce (relative to the dimension of the problem),…

Machine Learning · Statistics 2026-05-29 Darinka Dentcheva , Xiangyu Tian

In the face of adverse motives, it is indispensable to achieve a consensus. Elections have been the canonical way by which modern democracy has operated since the 17th century. Nowadays, they regulate markets, provide an engine for modern…

Machine Learning · Computer Science 2026-01-06 Hao Xiang Li , Yash Shah , Lorenzo Giusti

As the use of black-box models becomes ubiquitous in high stake decision-making systems, demands for fair and interpretable models are increasing. While it has been shown that interpretable models can be as accurate as black-box models in…

Machine Learning · Computer Science 2020-02-19 Ulrich Aïvodji , Julien Ferry , Sébastien Gambs , Marie-José Huguet , Mohamed Siala

We consider the problem of high-dimensional non-linear variable selection for supervised learning. Our approach is based on performing linear selection among exponentially many appropriately defined positive definite kernels that…

Machine Learning · Computer Science 2009-09-08 Francis Bach

The basic idea of voting protocols is that nodes query a sample of other nodes and adjust their own opinion throughout several rounds based on the proportion of the sampled opinions. In the classic model, it is assumed that all nodes have…

Probability · Mathematics 2021-01-28 Abraham Gutierrez , Sebastian Müller , Stjepan Šebek

Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introduces TE2Rules (Tree…

Machine Learning · Computer Science 2024-01-25 G Roshan Lal , Xiaotong Chen , Varun Mithal

We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time. This paper is a foundational and structural contribution: its goal is to define the core learning-theoretic…

Machine Learning · Computer Science 2026-03-25 Aomar Osmani

The goal of this paper is twofold. First and foremost, we aim to experimentally and quantitatively show that the choice of a multiwinner voting rule can play a crucial role on the way minorities are represented. We also test the possibility…

Computer Science and Game Theory · Computer Science 2016-04-11 Piotr Faliszewski , Jean-Francois Laslier , Robert Schaefer , Piotr Skowron , Arkadii Slinko , Nimrod Talmon

The field of machine have seen rising applications of equivariance criterion. However, there is no systematic way to justify its usage, including why it works, whether there is an optimal solution and if so, what form it carries. In this…

Statistics Theory · Mathematics 2025-09-23 Daowei Wang , Mian Wu , Haojin Zhou

This paper presents a novel theoretical framework for reducing the computational complexity of multi-model adaptive control/estimation systems through systematic transformation to controllable canonical form. While traditional multi-model…

Systems and Control · Electrical Eng. & Systems 2025-04-30 Farid Mafi , Ladan Khoshnevisan , Mohammad Pirani , Amir Khajepour

This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from…

Machine Learning · Computer Science 2015-09-16 Corinna Cortes , Prasoon Goyal , Vitaly Kuznetsov , Mehryar Mohri

This paper describes a generalizable model evaluation method that can be adapted to evaluate AI/ML models across multiple criteria including core scientific principles and more practical outcomes. Emerging from prediction competitions in…

Machine Learning · Computer Science 2024-03-19 Jason L. Harman , Jaelle Scheuerman

Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for…

Machine Learning · Computer Science 2023-01-24 Brett Mullins

We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, {The number of features here refers to the number of non-zero entries in the data matrix.}…

Machine Learning · Computer Science 2013-07-15 Alekh Agarwal , Olivier Chapelle , Miroslav Dudik , John Langford

We study approval-based committee voting from a novel perspective. While extant work largely centers around proportional representation of the voters, we shift our focus to the candidates while preserving proportionality. Intuitively,…

Computer Science and Game Theory · Computer Science 2025-06-24 Gregory Kehne , Ulrike Schmidt-Kraepelin , Krzysztof Sornat
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