:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Haoyu, Saab, Rayan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Numerical Analysis Probability
Online Access:	https://arxiv.org/abs/2412.18184
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Modified Equations for Stochastic Optimization
by: Perko, Stefan
Published: (2025)

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

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
by: Zhang, Haoyu, et al.
Published: (2025)

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

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

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

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

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

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
by: Paulin, Daniel, et al.
Published: (2024)

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

Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
by: Heiss, Jakob, et al.
Published: (2024)

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

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

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)

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

Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models
by: Reddy, Sohail, 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)

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

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

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

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)

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

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

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)

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)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)

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

Neural Operators Can Play Dynamic Stackelberg Games
by: Alvarez, Guillermo, et al.
Published: (2024)

Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
by: Furuya, Takashi, et al.
Published: (2024)

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)

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

On Bellman equations for continuous-time policy evaluation I: discretization and approximation
by: Mou, Wenlong, et al.
Published: (2024)

Probabilistic Analysis of Least Squares, Orthogonal Projection, and QR Factorization Algorithms Subject to Gaussian Noise
by: Lotfi, Ali, et al.
Published: (2024)

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)