Cover Image

Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Miranda, Charles, Schütte, Janina, Sommer, David, Eigel, Martin
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Probability Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Parallelized Midpoint Randomization for Langevin Monte Carlo
by: Yu, Lu, et al.
Published: (2024)

User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
by: Dalalyan, Arnak S., et al.
Published: (2017)

Convergence of Kinetic Langevin Monte Carlo on Lie groups
by: Kong, Lingkai, et al.
Published: (2024)

Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms
by: Neufeld, Ariel, et al.
Published: (2024)

Non-asymptotic analysis of Langevin-type Monte Carlo algorithms
by: Nakakita, Shogo
Published: (2023)

Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
by: Ghosh, Soumyadip, et al.
Published: (2021)

Complexity of Markov Chain Monte Carlo for Generalized Linear Models
by: Chak, Martin, et al.
Published: (2025)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
by: Balasubramanian, Krishnakumar, et al.
Published: (2025)

Sampling conditioned diffusions via Pathspace Projected Monte Carlo
by: Grafke, Tobias
Published: (2025)

Concentration of the Langevin Algorithm's Stationary Distribution
by: Altschuler, Jason M., et al.
Published: (2022)

Convergence Bounds for Sequential Monte Carlo on Multimodal Distributions using Soft Decomposition
by: Lee, Holden, et al.
Published: (2024)

High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
by: Liang, Tengyuan, et al.
Published: (2022)

Convergence rate of randomized midpoint Langevin Monte Carlo
by: Li, Ruinan, et al.
Published: (2025)

Time-Inhomogeneous Preconditioned Langevin Dynamics
by: Falk, Alexander, et al.
Published: (2026)

Efficient Sampling on Riemannian Manifolds via Langevin MCMC
by: Cheng, Xiang, et al.
Published: (2024)

Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
by: Yu, Zixiong, et al.
Published: (2024)

kTULA: A Langevin sampling algorithm with improved KL bounds under super-linear log-gradients
by: Lytras, Iosif, et al.
Published: (2025)

Non-asymptotic Analysis of Poisson randomized midpoint Langevin Monte Carlo
by: Shen, Tian, et al.
Published: (2025)

Tail-Sensitive KL and Rényi Convergence of Unadjusted Hamiltonian Monte Carlo via One-Shot Couplings
by: Bou-Rabee, Nawaf, et al.
Published: (2026)

On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
by: Durmus, Alain, et al.
Published: (2023)

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
by: Descours, Arnaud, et al.
Published: (2024)

Generating Rectifiable Measures through Neural Networks
by: Riegler, Erwin, et al.
Published: (2024)

Markov Chain Variance Estimation: A Stochastic Approximation Approach
by: Agrawal, Shubhada, et al.
Published: (2024)

Multivariate Gaussian Approximation for Random Forest via Region-based Stabilization
by: Shi, Zhaoyang, et al.
Published: (2024)

Improved Guarantees for Langevin Monte Carlo with Average Smoothness
by: Dalalyan, Arnak S., et al.
Published: (2026)

Sampling and estimation on manifolds using the Langevin diffusion
by: Bharath, Karthik, et al.
Published: (2023)

On Universality of Non-Separable Approximate Message Passing Algorithms
by: Lovig, Max, et al.
Published: (2025)

Pointwise Generalization in Deep Neural Networks
by: Li, Shaojie, et al.
Published: (2026)

Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev
by: Chewi, Sinho, et al.
Published: (2021)

Probabilistic Conformal Prediction with Approximate Conditional Validity
by: Plassier, Vincent, et al.
Published: (2024)

Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation
by: Schütte, Janina E., et al.
Published: (2024)

A Dynamical System View of Langevin-Based Non-Convex Sampling
by: Karimi, Mohammad Reza, et al.
Published: (2022)

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
by: Zhang, Yihan, et al.
Published: (2023)

Gaussian and Bootstrap Approximation for Matching-based Average Treatment Effect Estimators
by: Shi, Zhaoyang, et al.
Published: (2024)

Optimality of Approximate Message Passing Algorithms for Spiked Matrix Models with Rotationally Invariant Noise
by: Dudeja, Rishabh, et al.
Published: (2024)

Progressive Feedforward Collapse of ResNet Training
by: Wang, Sicong, et al.
Published: (2024)

Optimal Estimation in Orthogonally Invariant Generalized Linear Models: Spectral Initialization and Approximate Message Passing
by: Zhang, Yihan, et al.
Published: (2026)

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
by: Rangriz, Parsa
Published: (2025)

Geometric Gaussian Approximations of Probability Distributions
by: Da Costa, Nathaël, et al.
Published: (2025)

ResNets of All Shapes and Sizes: Convergence of Training Dynamics in the Large-scale Limit
by: Chaintron, Louis-Pierre, et al.
Published: (2026)