Image de couverture de livre

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Paulin, Daniel, Whalley, Peter A., Chada, Neil K., Leimkuhler, Benedict
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Numerical Analysis Probability Computation Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
par: Chada, Neil K., et autres
Publié: (2023)

Kinetic Langevin Splitting Schemes for Constrained Sampling
par: Chada, Neil K., et autres
Publié: (2026)

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
par: Paulin, Daniel, et autres
Publié: (2024)

Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs
par: Awadelkarim, Elsiddig, et autres
Publié: (2024)

Contraction and Convergence Rates for Discretized Kinetic Langevin Dynamics
par: Leimkuhler, Benedict, et autres
Publié: (2023)

Cluster Formation in Diffusive Systems
par: Leimkuhler, Benedict, et autres
Publié: (2025)

Random Reshuffling for Stochastic Gradient Langevin Dynamics
par: Shaw, Luke, et autres
Publié: (2025)

Provable Diffusion Posterior Sampling for Bayesian Inversion
par: Chang, Jinyuan, et autres
Publié: (2025)

The Ensemble Kalman Filter for Dynamic Inverse Problems
par: Weissmann, Simon, et autres
Publié: (2024)

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
par: Duan, Chenguang, et autres
Publié: (2026)

Riemannian Langevin Dynamics: Strong Convergence of Geometric Euler-Maruyama Scheme
par: Zhan, Zhiyuan, et autres
Publié: (2026)

Bayesian Deep Learning with Multilevel Trace-class Neural Networks
par: Chada, Neil K., et autres
Publié: (2022)

Second Order Ensemble Langevin Method for Sampling and Inverse Problems
par: Liu, Ziming, et autres
Publié: (2022)

Sampling and estimation on manifolds using the Langevin diffusion
par: Bharath, Karthik, et autres
Publié: (2023)

Unified Stochastic Framework for Neural Network Quantization and Pruning
par: Zhang, Haoyu, et autres
Publié: (2024)

Theoretical guarantees for stochastic gradient sampling methods via Gaussian convolution inequalities
par: Paulin, Daniel, et autres
Publié: (2026)

Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression
par: Miyatake, Yuto, et autres
Publié: (2024)

Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
par: Krach, Florian, et autres
Publié: (2022)

Why Cannot Neural Networks Master Extrapolation? Insights from Physical Laws
par: Dakhmouche, Ramzi, et autres
Publié: (2025)

Convergence of Kinetic Langevin Monte Carlo on Lie groups
par: Kong, Lingkai, et autres
Publié: (2024)

Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped
par: Kim, Kyurae, et autres
Publié: (2025)

Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
par: Malpica-Morales, Antonio, et autres
Publié: (2024)

Median of Means Sampling for the Keister Function
par: Zhang, Bocheng
Publié: (2025)

Convergence of kinetic Langevin samplers for non-convex potentials
par: Schuh, Katharina, et autres
Publié: (2024)

Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
par: Zhang, Rui, et autres
Publié: (2023)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
par: Lim, Dong-Young, et autres
Publié: (2022)

Stein transport for Bayesian inference
par: Nüsken, Nikolas
Publié: (2024)

Splitting AVF method for generalized Langevin equations: probability density function and geometric ergodicity
par: Dai, Xinjie, et autres
Publié: (2026)

Bayesian Hierarchical Models and the Maximum Entropy Principle
par: Brewer, Brendon J.
Publié: (2026)

Reliable data clustering with Bayesian community detection
par: Neuman, Magnus, et autres
Publié: (2025)

Contractive kinetic Langevin samplers beyond global Lipschitz continuity
par: Lytras, Iosif, et autres
Publié: (2025)

Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity
par: Luo, Hengrui, et autres
Publié: (2026)

Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
par: Rajpal, Rajit, et autres
Publié: (2025)

Nonparametric estimation of homogenized invariant measures from multiscale data via Hermite expansion
par: Borodavka, Jaroslav I., et autres
Publié: (2025)

Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
par: Tong, Xin T., et autres
Publié: (2026)

Regression-aware decompositions
par: Tygert, Mark
Publié: (2017)

Multivariate Density Estimation via Variance-Reduced Sketching
par: Peng, Yifan, et autres
Publié: (2024)

Variational Markov chain mixtures with automatic component selection
par: Miles, Christopher E., et autres
Publié: (2024)

Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
par: Li, Zhaohui, et autres
Publié: (2022)

Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration
par: Ehrhardt, Emily C., et autres
Publié: (2025)