:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Sankoh, Aroon, Wickerhauser, Victor
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Probability
Online Access:	https://arxiv.org/abs/2505.03980
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fisher information dissipation for time inhomogeneous stochastic differential equations
by: Feng, Qi, et al.
Published: (2024)

The generator gradient estimator is an adjoint state method for stochastic differential equations
by: Badolle, Quentin, et al.
Published: (2024)

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

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

Effective continuous equations for adaptive SGD: a stochastic analysis view
by: Callisti, Luca, et al.
Published: (2025)

Neural stochastic Volterra equations: learning path-dependent dynamics
by: Bergerhausen, Martin, et al.
Published: (2024)

Active operator learning with predictive uncertainty quantification for partial differential equations
by: Winovich, Nick, et al.
Published: (2025)

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

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

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

Extraction of informative statistical features in the problem of forecasting time series generated by It{ô}-type processes
by: Korolev, Victor, et al.
Published: (2026)

Comparative study of machine learning and statistical methods for automatic identification and quantification in γ-ray spectrometry
by: Phan, Dinh Triem, et al.
Published: (2025)

Approximating G(t)/GI/1 queues with deep learning
by: Sherzer, Eliran, et al.
Published: (2024)

Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
by: Messuti, Giovanni, et al.
Published: (2024)

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

High Rank Path Development: an approach of learning the filtration of stochastic processes
by: Tao, Jiajie, et al.
Published: (2024)

Predicting path-dependent processes by deep learning
by: Zheng, Xudong, et al.
Published: (2024)

Off-the-grid learning of mixtures from a continuous dictionary
by: Butucea, Cristina, et al.
Published: (2022)

Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
by: Winkler, Ludwig, et al.
Published: (2024)

Lipschitz continuity in the Hurst parameter of functionals of stochastic differential equations driven by a fractional Brownian motion
by: Richard, Alexandre, et al.
Published: (2016)

Score matching for bridges without learning time-reversals
by: Baker, Elizabeth L., et al.
Published: (2024)

Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
by: Butucea, Cristina, et al.
Published: (2022)

Efficient estimation of jump parameters for stochastic differential equations driven by L{é}vy processes
by: Bayraktar, Elise, et al.
Published: (2025)

Finite sample bounds for barycenter estimation in geodesic spaces
by: Brunel, Victor-Emmanuel, et al.
Published: (2025)

Long-time asymptotics of noisy SVGD outside the population limit
by: Priser, Victor, et al.
Published: (2024)

A distance function for stochastic matrices
by: Lee, Antony R., et al.
Published: (2024)

Towards certification: A complete statistical validation pipeline for supervised learning in industry
by: Lacasa, Lucas, et al.
Published: (2024)

Extreme Value theory and Poisson statistics for discrete time samplings of stochastic differential equations
by: Flandoli, F., et al.
Published: (2023)

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)

Tunable correlation retention: A statistical method for generating synthetic data
by: Jävergård, Nicklas, et al.
Published: (2024)

Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
by: Li, Yang, et al.
Published: (2023)

Small coresets via negative dependence: DPPs, linear statistics, and concentration
by: Bardenet, Rémi, et al.
Published: (2024)

Exploring how deep learning decodes anomalous diffusion via Grad-CAM
by: Bae, Jaeyong, et al.
Published: (2024)

Universal approximation property of neural stochastic differential equations
by: Kwossek, Anna P., et al.
Published: (2025)

An information field theory approach to Bayesian state and parameter estimation in dynamical systems
by: Hao, Kairui, et al.
Published: (2023)

Spectral alignment of stochastic gradient descent for high-dimensional classification tasks
by: Arous, Gerard Ben, et al.
Published: (2023)

Non-asymptotic statistical test of the diffusion coefficient of stochastic differential equations
by: Melnykova, Anna, et al.
Published: (2023)

Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
by: Hassanaly, Malik, et al.
Published: (2026)

Nonparametric learning of stochastic differential equations from sparse and noisy data
by: Ganguly, Arnab, et al.
Published: (2025)

Large-width functional asymptotics for deep Gaussian neural networks
by: Bracale, Daniele, et al.
Published: (2021)