Image de couverture de livre

Adaptive Kernel Selection for Stein Variational Gradient Descent

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Melcher, Moritz, Weissmann, Simon, Wilson, Ashia C., Zech, Jakob
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation
par: Weissmann, Simon, et autres
Publié: (2024)

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
par: Kassing, Sebastian, et autres
Publié: (2025)

Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
par: Kawamura, Yuya, et autres
Publié: (2024)

Finite-Particle Rates for Regularized Stein Variational Gradient Descent
par: He, Ye, et autres
Publié: (2026)

Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification
par: Tabor, Griffin, et autres
Publié: (2025)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
par: Padmanabha, Govinda Anantha, et autres
Publié: (2024)

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent
par: Banerjee, Sayan, et autres
Publié: (2024)

Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents
par: Liu, Xiaotong, et autres
Publié: (2026)

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
par: Della Libera, Luca, et autres
Publié: (2024)

Branching Stein Variational Gradient Descent for sampling multimodal distributions
par: Bañales, Isaías, et autres
Publié: (2025)

Low Stein Discrepancy via Message-Passing Monte Carlo
par: Kirk, Nathan, et autres
Publié: (2025)

An Approximate Ascent Approach To Prove Convergence of PPO
par: Doering, Leif, et autres
Publié: (2026)

Adaptive Backtracking Line Search
par: Cavalcanti, Joao V., et autres
Publié: (2024)

Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion
par: Corrales, Miguel, et autres
Publié: (2024)

A Stein Gradient Descent Approach for Doubly Intractable Distributions
par: Lee, Heesang, et autres
Publié: (2024)

Multirate Stein Variational Gradient Descent for Efficient Bayesian Sampling
par: Sarshar, Arash
Publié: (2026)

The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models
par: Lev, Omri, et autres
Publié: (2024)

Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
par: Klein, Sara, et autres
Publié: (2023)

Learning Curves of Stochastic Gradient Descent in Kernel Regression
par: Zhang, Haihan, et autres
Publié: (2025)

The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT
par: Zhu, Xin, et autres
Publié: (2024)

Semivalue-based data valuation is arbitrary and gameable
par: Diehl, Hannah, et autres
Publié: (2025)

Stochastic Adaptive Gradient Descent Without Descent
par: Aujol, Jean-François, et autres
Publié: (2025)

Position: AI Evaluations Should be Grounded on a Theory of Capability
par: Jo, Nathanael, et autres
Publié: (2025)

Adaptive Conditional Gradient Descent
par: Khademi, Abbas, et autres
Publié: (2025)

Regularized Stein Variational Gradient Flow
par: He, Ye, et autres
Publié: (2022)

Efficient and accurate steering of Large Language Models through attention-guided feature learning
par: Davarmanesh, Parmida, et autres
Publié: (2026)

Variational Stochastic Gradient Descent for Deep Neural Networks
par: Chen, Haotian, et autres
Publié: (2024)

Sequential Kernelized Stein Discrepancy
par: Martinez-Taboada, Diego, et autres
Publié: (2024)

Mean-field underdamped Langevin dynamics and its spacetime discretization
par: Fu, Qiang, et autres
Publié: (2023)

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
par: Pielok, Tobias, et autres
Publié: (2025)

Stochastic Gradient Descent with Adaptive Data
par: Che, Ethan, et autres
Publié: (2024)

ELBOing Stein: Variational Bayes with Stein Mixture Inference
par: Rønning, Ola, et autres
Publié: (2024)

Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging
par: AlQuabeh, Hilal, et autres
Publié: (2024)

Quantum Shadow Gradient Descent for Variational Quantum Algorithms
par: Heidari, Mohsen, et autres
Publié: (2023)

Structure Matters: Dynamic Policy Gradient
par: Klein, Sara, et autres
Publié: (2024)

Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent
par: Kumar, Mohit, et autres
Publié: (2024)

Mathematical theory of deep learning
par: Petersen, Philipp, et autres
Publié: (2024)

Controlling Moments with Kernel Stein Discrepancies
par: Kanagawa, Heishiro, et autres
Publié: (2022)

Adaptive Heavy-Tailed Stochastic Gradient Descent
par: Gong, Bodu, et autres
Publié: (2025)

High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm
par: Srinivasan, Vishwak, et autres
Publié: (2024)