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Found 11 papers, 3 papers with code
SDEs for Minimax Optimization
Minimax optimization problems have attracted a lot of attention over the past few years, with applications ranging from economics to machine learning.
An SDE for Modeling SAM: Theory and Insights
We study the SAM (Sharpness-Aware Minimization) optimizer which has recently attracted a lot of interest due to its increased performance over more classical variants of stochastic gradient descent.
On the Theoretical Properties of Noise Correlation in Stochastic Optimization
This generalizes processes based on Brownian motion, such as the Ornstein-Uhlenbeck process.
Explicit Regularization in Overparametrized Models via Noise Injection
Injecting noise within gradient descent has several desirable features, such as smoothing and regularizing properties.
Anticorrelated Noise Injection for Improved Generalization
Injecting artificial noise into gradient descent (GD) is commonly employed to improve the performance of machine learning models.
A Fourier State Space Model for Bayesian ODE Filters
Gaussian ODE filtering is a probabilistic numerical method to solve ordinary differential equations (ODEs).
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
To address this shortcoming, we employ Gaussian ODE filtering (a probabilistic numerical method for ODEs) to construct a local Gaussian approximation to the likelihood.
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions.
Convergence Rates of Gaussian ODE Filters
A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems.
Bayesian Filtering for ODEs with Bounded Derivatives
Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e. g. if the vector field or the initial value is only approximately known or evaluable.
Active Uncertainty Calibration in Bayesian ODE Solvers
There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates.
