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AI Blogs
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics Gaetano Romano, Idris A. Eckley, Paul Fearnhead, Guillem Rigaill; 24(81):1−36, 2023. Abstract The ability to process high-frequency observations with limited computational resources is crucial for modern applications of online changepoint detection.…
The content can be rewritten as follows: arXivLabs is a platform that enables collaborators to create and share innovative features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs have embraced and embraced our core…
DART: Distance Assisted Recursive Testing Xuechan Li, Anthony D. Sung, Jichun Xie; 24(169):1−41, 2023. Abstract In the field of modern data science, multiple testing is a widely used tool. In certain cases, the hypotheses are organized within a space where…
arXivLabs is a platform designed for collaborative development and sharing of new features on the arXiv website. Both individuals and organizations that engage with arXivLabs are aligned with our core principles of openness, community, excellence, and user data privacy. We…
Bayes-Newton Methods for Approximate Bayesian Inference with Positive Semi-Definite Guarantees Authors: William J. Wilkinson, Simo Särkkä, Arno Solin; Volume 24, Issue 83, Pages 1-50, 2023. Abstract In this study, we propose a framework that formulates natural gradient variational inference (VI),…
The popularity of decentralized federated learning (DFL) has increased due to its practicality in various applications. However, training a shared model among a large number of nodes in DFL is more challenging compared to the centralized version. This is because…
Universal Metric Embeddings with Small Transformers Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić; 24(170):1−48, 2023. Abstract This study focuses on representing data from an arbitrary metric space $\mathcal{X}$ in the space of univariate Gaussian mixtures equipped with a transport metric (Delon…
[Submitted on 31 Aug 2023] Download a PDF of the paper titled “What can we learn from quantum convolutional neural networks?” by Chukwudubem Umeano and 3 other authors Download PDF Abstract: This paper analyzes quantum convolutional neural networks (QCNNs) and…
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence Authors: Henry Lam, Haofeng Zhang; Journal of Machine Learning Research, 24(85):1−58, 2023. Abstract The standard Monte Carlo computation is well-known for its canonical square-root convergence speed in terms…
arXivLabs is a platform where collaborators can develop and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs share our values of openness, community, excellence, and user data privacy. We are committed…
