Image de couverture de livre

Online Regularized Learning Algorithms in RKHS with $β$- and $ϕ$-Mixing Sequences

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Roy, Priyanka, Saminger-Platz, Susanne
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Functional Analysis 60J20, 68T05, 68Q32, 62L20, 62H05
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Gradient Descent Algorithm in Hilbert Spaces under Stationary Markov Chains with $ϕ$- and $β$-Mixing
par: Roy, Priyanka, et autres
Publié: (2025)

Markov Chain Gradient Descent in Hilbert Spaces
par: Roy, Priyanka, et autres
Publié: (2024)

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
par: Bai, Jinhui, et autres
Publié: (2025)

Parametrising the Inhomogeneity Inducing Capacity of a Training Set, and its Impact on Supervised Learning
par: Roy, Gargi, et autres
Publié: (2025)

Truncated Kernel Stochastic Gradient Descent on Spheres
par: Bai, Jinhui, et autres
Publié: (2024)

Retrieval-Augmented Memory for Online Learning
par: Du, Wenzhang
Publié: (2025)

Spectral Algorithms under Covariate Shift
par: Fan, Jun, et autres
Publié: (2025)

Weak Signals and Heavy Tails: Learning Theory meets Extreme Value Analysis
par: Clémençon, Stephan, et autres
Publié: (2025)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
par: Dehshiri, Mahdi, et autres
Publié: (2026)

Transient anisotropic kernel for probabilistic learning on manifolds
par: Soize, Christian, et autres
Publié: (2024)

Least squares approximations in linear statistical inverse learning problems
par: Helin, Tapio
Publié: (2022)

Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
par: Krishnamurthy, Sanath Kumar, et autres
Publié: (2024)

Conformal e-testing
par: Vovk, Vladimir, et autres
Publié: (2020)

Error Bounds for Learning with Vector-Valued Random Features
par: Lanthaler, Samuel, et autres
Publié: (2023)

MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation
par: Ariq, Ahanaf Hasan
Publié: (2026)

Physics-informed features in supervised machine learning
par: Lampani, Margherita, et autres
Publié: (2025)

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses
par: Li, Haoran
Publié: (2025)

Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
par: Awan, Jordan, et autres
Publié: (2018)

Validity and efficiency of the conformal CUSUM procedure
par: Vovk, Vladimir, et autres
Publié: (2024)

Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines
par: Fokoué, Ernest
Publié: (2026)

Gaussian processes in Non-commutative probability
par: Jorgensen, Palle E. T., et autres
Publié: (2024)

Operator Learning Using Random Features: A Tool for Scientific Computing
par: Nelsen, Nicholas H., et autres
Publié: (2024)

Imbalanced Classification under Capacity Constraints
par: Fraiman, Daniel, et autres
Publié: (2026)

The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
par: Borkar, Vivek, et autres
Publié: (2021)

Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
par: Ayday, Nil, et autres
Publié: (2026)

Pair Correlation Factor and the Sample Complexity of Gaussian Mixtures
par: Aryan, Farzad
Publié: (2025)

Extensions of the regret-minimization algorithm for optimal design
par: Chen, Youguang, et autres
Publié: (2025)

Asymptotic independence in higher dimensions and its implications on risk management
par: Das, Bikramjit, et autres
Publié: (2024)

On-Average Stability of Multipass Preconditioned SGD and Effective Dimension
par: Vary, Simon, et autres
Publié: (2026)

Batched Nonparametric Bandits via k-Nearest Neighbor UCB
par: Arya, Sakshi
Publié: (2025)

The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
par: Lauand, Caio Kalil, et autres
Publié: (2023)

Revisiting Step-Size Assumptions in Stochastic Approximation
par: Lauand, Caio Kalil, et autres
Publié: (2024)

Machine Collaboration
par: Liu, Qingfeng, et autres
Publié: (2021)

Model-free filtering in high dimensions via projection and score-based diffusions
par: Christensen, Sören, et autres
Publié: (2025)

Realizable Bayes-Consistency for General Metric Losses
par: Cohen, Dan Tsir, et autres
Publié: (2026)

Uniform-in-time convergence bounds for Persistent Contrastive Divergence Algorithms
par: Oliva, Paul Felix Valsecchi, et autres
Publié: (2025)

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming
par: Wu, Hao
Publié: (2026)

Fused Multinomial Logistic Regression Utilizing Summary-Level External Machine-learning Information
par: Dai, Chi-Shian, et autres
Publié: (2026)

Two approaches to multiple canonical correlation analysis for repeated measures data
par: Górecki, Tomasz, et autres
Publié: (2025)

Constructive interpolation and generalization rates for neural ODEs: a control perspective
par: Álvarez-López, Antonio, et autres
Publié: (2026)