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Found 5 papers, 3 papers with code
Policy Gradient Optimal Correlation Search for Variance Reduction in Monte Carlo simulation and Maximum Optimal Transport
We propose a new algorithm for variance reduction when estimating $f(X_T)$ where $X$ is the solution to some stochastic differential equation and $f$ is a test function.
A note on $L^1$-Convergence of the Empiric Minimizer for unbounded functions with fast growth
For $V : \mathbb{R}^d \to \mathbb{R}$ coercive, we study the convergence rate for the $L^1$-distance of the empiric minimizer, which is the true minimum of the function $V$ sampled with noise with a finite number $n$ of samples, to the minimum of $V$.
Langevin algorithms for very deep Neural Networks with application to image classification
Training a very deep neural network is a challenging task, as the deeper a neural network is, the more non-linear it is.
Langevin algorithms for Markovian Neural Networks and Deep Stochastic control
Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep.
Convergence rates of Gibbs measures with degenerate minimum
We assume instead that the minimum is strictly polynomial and give a higher order nested expansion of $f$ at $x^\star$, which depends on every coordinate.
Probability
