Cover Image

Effective continuous equations for adaptive SGD: a stochastic analysis view

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Callisti, Luca, Romito, Marco, Triggiano, Francesco
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle
by: Giovagnini, Filippo, et al.
Published: (2026)

Dissipative solutions to randomly forced 3D Euler equations
by: Pappalettera, Umberto, et al.
Published: (2025)

Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations
by: Sankoh, Aroon, et al.
Published: (2025)

Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models
by: Agazzi, Andrea, et al.
Published: (2026)

Non-uniqueness of weak solutions for a logarithmically supercritical hyperdissipative Navier-Stokes system
by: Romito, Marco, et al.
Published: (2024)

Averaging Dynamics and Wong-Zakai approximations for a Fast-Slow Navier-Stokes System Driven by fractional Brownian Motion
by: Luongo, Eliseo, et al.
Published: (2026)

Error estimates between SGD with momentum and underdamped Langevin diffusion
by: Guillin, Arnaud, et al.
Published: (2024)

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

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

Convergence, Sticking and Escape: Stochastic Dynamics Near Critical Points in SGD
by: Dudukalov, Dmitry, et al.
Published: (2025)

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
by: Wan, Yijun, et al.
Published: (2023)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
by: Tanguy, Eloi
Published: (2023)

Concept activation vectors: a unifying view and adversarial attacks
by: Schnoor, Ekkehard, et al.
Published: (2025)

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

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

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

$2D$ vorticity Euler equations: Superposition solutions and nonlinear Markov processes
by: Rehmeier, Marco, 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)

A characterization of sample adaptivity in UCB data
by: Chen, Yilun, et al.
Published: (2025)

Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model
by: Lebeau, Hugo, et al.
Published: (2024)

Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry
by: Armacki, Aleksandar, et al.
Published: (2024)

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

From Betting to Empirical Bernstein LIL
by: Orabona, Francesco
Published: (2026)

Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
by: Agrawalla, Bhavya, et al.
Published: (2023)

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

Joint stochastic localization and applications
by: Alberts, Tom, et al.
Published: (2025)

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

Asymptotic regularity of a generalised stochastic Halpern scheme
by: Pischke, Nicholas, et al.
Published: (2024)

Fully lifted \emph{blirp} interpolation -- a large deviation view
by: Stojnic, Mihailo
Published: (2025)

A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
by: Zhang, Yixuan, et al.
Published: (2025)

High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)

SDEs with singular coefficients: The martingale problem view and the stochastic dynamics view
by: Issoglio, Elena, 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)

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

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

An abstract effective convergence theorem for stochastic processes, with applications to stochastic approximation
by: Neri, Morenikeji, et al.
Published: (2025)

Student-t processes as infinite-width limits of posterior Bayesian neural networks
by: Caporali, Francesco, et al.
Published: (2025)

Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
by: Di Persio, Luca, et al.
Published: (2026)

Geometric regularization of autoencoders via observed stochastic dynamics
by: Hill, Sean, et al.
Published: (2026)