Cover Image

Markov Chain Gradient Descent in Hilbert Spaces

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Roy, Priyanka, Saminger-Platz, Susanne
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Functional Analysis 60J20, 68T05, 68Q32, 62L20
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gradient Descent Algorithm in Hilbert Spaces under Stationary Markov Chains with $ϕ$- and $β$-Mixing
by: Roy, Priyanka, et al.
Published: (2025)

Online Regularized Learning Algorithms in RKHS with $β$- and $ϕ$-Mixing Sequences
by: Roy, Priyanka, et al.
Published: (2025)

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
by: Bai, Jinhui, et al.
Published: (2025)

Truncated Kernel Stochastic Gradient Descent on Spheres
by: Bai, Jinhui, et al.
Published: (2024)

Parametrising the Inhomogeneity Inducing Capacity of a Training Set, and its Impact on Supervised Learning
by: Roy, Gargi, et al.
Published: (2025)

Physics-informed features in supervised machine learning
by: Lampani, Margherita, et al.
Published: (2025)

Retrieval-Augmented Memory for Online Learning
by: Du, Wenzhang
Published: (2025)

Transient anisotropic kernel for probabilistic learning on manifolds
by: Soize, Christian, et al.
Published: (2024)

Spectral Algorithms under Covariate Shift
by: Fan, Jun, et al.
Published: (2025)

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

A Theory of Non-Acyclic Generative Flow Networks
by: Brunswic, Leo Maxime, et al.
Published: (2023)

Large Spikes in Stochastic Gradient Descent: A Large-Deviations View
by: Gess, Benjamin, et al.
Published: (2026)

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

$ε$-Policy Gradient for Online Pricing
by: Szpruch, Lukasz, et al.
Published: (2024)

Error Bounds for Learning with Vector-Valued Random Features
by: Lanthaler, Samuel, et al.
Published: (2023)

On-Average Stability of Multipass Preconditioned SGD and Effective Dimension
by: Vary, Simon, et al.
Published: (2026)

The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
by: Borkar, Vivek, et al.
Published: (2021)

Validity and efficiency of the conformal CUSUM procedure
by: Vovk, Vladimir, et al.
Published: (2024)

Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation
by: Yang, Peiqi, et al.
Published: (2025)

Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
by: Krishnamurthy, Sanath Kumar, et al.
Published: (2024)

Operator Learning Using Random Features: A Tool for Scientific Computing
by: Nelsen, Nicholas H., et al.
Published: (2024)

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

Gaussian processes in Non-commutative probability
by: Jorgensen, Palle E. T., et al.
Published: (2024)

Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
by: Baldassari, Lorenzo, et al.
Published: (2025)

Revisiting Step-Size Assumptions in Stochastic Approximation
by: Lauand, Caio Kalil, et al.
Published: (2024)

The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
by: Lauand, Caio Kalil, et al.
Published: (2023)

Realizable Bayes-Consistency for General Metric Losses
by: Cohen, Dan Tsir, et al.
Published: (2026)

Convergence of Markov Chains for Constant Step-size Stochastic Gradient Descent with Separable Functions
by: Shirokoff, David, et al.
Published: (2024)

Random feature-based double Vovk-Azoury-Warmuth algorithm for online multi-kernel learning
by: Rokhlin, Dmitry B., et al.
Published: (2025)

A hierarchical Vovk-Azoury-Warmuth forecaster with discounting for online regression in RKHS
by: Rokhlin, Dmitry B.
Published: (2025)

A Special Case of Quadratic Extrapolation Under the Neural Tangent Kernel
by: Kim, Abiel
Published: (2025)

A Function-Space Stability Boundary for Generalization in Interpolating Learning Systems
by: Katende, Ronald
Published: (2026)

Conformal e-prediction
by: Vovk, Vladimir
Published: (2020)

Adversarial flows: A gradient flow characterization of adversarial attacks
by: Weigand, Lukas, et al.
Published: (2024)

Conformal e-testing
by: Vovk, Vladimir, et al.
Published: (2020)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
by: Dehshiri, Mahdi, et al.
Published: (2026)

Fibonacci-Driven Recursive Ensembles: Algorithms, Convergence, and Learning Dynamics
by: Fokoué, Ernest
Published: (2026)

Inductive randomness predictors: beyond conformal
by: Vovk, Vladimir
Published: (2025)

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

Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks
by: Lam, Samuel Chun-Hei, et al.
Published: (2025)