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Uncertainty in Machine Learning
Hans Weytjens, Wouter Verbeke
https://arxiv.org/abs/2510.06007 https://arxiv.org/pdf/2510.06007

Uncertainty in Machine Learning
This book chapter introduces the principles and practical applications of uncertainty quantification in machine learning. It explains how to identify and distinguish between different types of uncertainty and presents methods for quantifying uncertainty in predictive models, including linear regression, random forests, and neural networks. The chapter also covers conformal prediction as a framework for generating predictions with predefined confidence intervals. Finally, it explores how uncerta…

Applications of the Vendi score in genomic epidemiology
Bjarke Frost Nielsen, Amey P. Pasarkar, Qiqi Yang, Bryan T. Grenfell, Adji Bousso Dieng
https://arxiv.org/abs/2509.22520 …

Applications of the Vendi score in genomic epidemiology
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the metrics commonly used in ecology. It is classification-independent, incorporates abundance information, and has a tunable sensitivity to rare/abundant types. Using rich COVID-19 sequence data as a paradigm, we develop methods for applying the VS to time-resolved sequence data. We show how the VS allows for characteriz…

Application of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors
Vedant Sawal, Hiu Yung Wong
https://arxiv.org/abs/2509.11113 https://

Application of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors
This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation framework, we model and analyze six spatial defect types-circular, circular-complement, ring, row, column, and checkerboard-across multiple layers of a multi-array neuromorphic architecture. We demonstrate that the proposed correction method, which employs a lightweight neural ne…