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AI Blogs
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation Qian Li, Binyan Jiang, Defeng Sun; 24(134):1−44, 2023. Abstract The estimation of precision matrices, also known as inverse covariance matrices, is crucial in statistical data analysis and machine learning.…
Adaptation to the Range in K-Armed Bandits Authors: Hédi Hadiji, Gilles Stoltz; Publication Date: 2023; Pages: 1-33 Abstract This study focuses on stochastic bandit problems involving K arms, each associated with a distribution supported on a given finite range [m,…
Sparse GCA and Thresholded Gradient Descent Authors: Sheng Gao, Zongming Ma; Published: 2023; Volume: 24(135); Pages: 1-61. Abstract Sparse GCA and Thresholded Gradient Descent is a study on uncovering linear relationships across multiple data sets. It extends canonical correlation analysis,…
Extending Adversarial Attacks to Generate Adversarial Class Probability Distributions Authors: Jon Vadillo, Roberto Santana, Jose A. Lozano; Published in 2023, Volume 24, Pages 1-42. Abstract Deep learning models have shown remarkable performance and generalization capabilities in various artificial intelligence tasks.…
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection Wenhao Li, Ningyuan Chen, L. Jeff Hong; 24(136):1−84, 2023. Abstract This study addresses the problem of dimension reduction in contextual online learning (multi-armed bandit) with high-dimensional covariates $x$ and decision…
Learning Mean-Field Games with Discounted and Average Costs Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi; 24(17):1−59, 2023. Abstract This study focuses on learning approximate Nash equilibria for discrete-time mean-field games with stochastic nonlinear state dynamics subject to both average and…
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks Hui Jin, Guido Montufar; Volume 24, Issue 137, Pages 1-97, 2023. Abstract This study focuses on investigating the training of wide neural networks using gradient…
Regularized Joint Mixture Models Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee; 24(19):1−47, 2023. Abstract Regularized regression models have been extensively studied and, when certain conditions are met, they provide fast and statistically interpretable results. However, in many applications, large…
Asymptotics of Network Embeddings Learned via Subsampling Authors: Andrew Davison, Morgane Austern; Publication Date: 2023; Journal: 24(138):1−120 Abstract Network data plays a crucial role in modern machine learning, with various tasks such as node classification, node clustering, and link prediction.…
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