Cover Image

Modified Equations for Stochastic Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Perko, Stefan
Format:	Preprint
Published:	2025
Subjects:	Probability Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Unified Stochastic Framework for Neural Network Quantization and Pruning
by: Zhang, Haoyu, et al.
Published: (2024)

Towards Continuous-Time Approximations for Stochastic Gradient Descent without Replacement
by: Perko, Stefan
Published: (2025)

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
by: Duan, Chenguang, et al.
Published: (2026)

Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023)

Weak Generative Sampler to Efficiently Sample Invariant Distribution of Stochastic Differential Equation
by: Cai, Zhiqiang, et al.
Published: (2024)

Why Cannot Neural Networks Master Extrapolation? Insights from Physical Laws
by: Dakhmouche, Ramzi, et al.
Published: (2025)

Scale-Adaptive Generative Flows for Multiscale Scientific Data
by: Chen, Yifan, et al.
Published: (2025)

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
by: Neufeld, Ariel, et al.
Published: (2024)

New Trends in the Stability of Sinkhorn Semigroups
by: Del Moral, Pierre, et al.
Published: (2026)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation
by: Jentzen, Arnulf, et al.
Published: (2021)

Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
by: Krach, Florian, et al.
Published: (2022)

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
by: Paulin, Daniel, et al.
Published: (2024)

Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature
by: Hayakawa, Satoshi
Published: (2026)

Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
by: Neufeld, Ariel, et al.
Published: (2024)

Riemannian Langevin Dynamics: Strong Convergence of Geometric Euler-Maruyama Scheme
by: Zhan, Zhiyuan, et al.
Published: (2026)

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
by: Andersson, William, et al.
Published: (2023)

A Mean Field Ansatz for Zero-Shot Weight Transfer
by: Chen, Xingyuan, et al.
Published: (2024)

Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
by: Zhang, Rui, et al.
Published: (2023)

Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
by: He, Ye, et al.
Published: (2026)

Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
by: Iske, Meira, et al.
Published: (2026)

Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models
by: Reddy, Sohail, et al.
Published: (2024)

Data-driven approximation of transfer operators for mean-field stochastic differential equations
by: Ioannou, Eirini, et al.
Published: (2025)

Strong Stability Preservation for Stochastic Partial Differential Equations
by: Woodfield, James
Published: (2024)

Space-time deep neural network approximations for high-dimensional partial differential equations
by: Hornung, Fabian, et al.
Published: (2020)

Hybrid Stochastic Functional Differential Equations with Infinite Delay: Approximations and Numerics
by: Li, Guozhen, et al.
Published: (2025)

L2 convergence of smooth approximations of Stochastic Differential Equations with unbounded coefficients
by: Pathiraja, Sahani
Published: (2020)

Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped
by: Kim, Kyurae, et al.
Published: (2025)

Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation
by: Chen, Yifan, et al.
Published: (2024)

Single-seed generation of Brownian paths and integrals for adaptive and high order SDE solvers
by: Jelinčič, Andraž, et al.
Published: (2024)

Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration
by: Ehrhardt, Emily C., et al.
Published: (2025)

Sufficient Conditions for the Energy Balance for the Stochastic Incompressible Euler Equations with Additive Noise in two Space Dimensions
by: Rohner, Tobias, et al.
Published: (2025)

Hierarchical Gaussian Random Fields for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and The Karhunen-Loève Decomposition
by: Reddy, Sohail
Published: (2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)

Physics-Informed Inference Time Scaling for Solving High-Dimensional PDE via Defect Correction
by: Fan, Zexi, et al.
Published: (2025)

DeepMartingale: Duality of the Optimal Stopping Problem with Expressivity and High-Dimensional Hedging
by: Ye, Junyan, et al.
Published: (2025)

Provable Diffusion Posterior Sampling for Bayesian Inversion
by: Chang, Jinyuan, et al.
Published: (2025)

Samplet limits and multiwavelets
by: Giacchi, Gianluca, et al.
Published: (2026)

Analysis of singular subspaces under random perturbations
by: Wang, Ke
Published: (2024)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
by: Neufeld, Ariel, et al.
Published: (2024)