variance-reduction

Providing reproducibility in deep learning frameworks

Chaospy - Toolbox for performing uncertainty quantification.

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

A model free Monte Carlo approach to price and hedge American options equiped with Heston model, OHMC, and LSM

Riemannian stochastic optimization algorithms: Version 1.0.3

Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution, AAAI 2020 and NeurIPS 2019 Bayesian Deep Learning Workshop

Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)

A platform for distributed optimization expriments using OpenMPI

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Variance reduction in energy estimators accelerates the exponential convergence in deep learning (ICLR'21)

In this paper, we propose Filter Gradient Decent (FGD), an efficient stochastic optimization algorithm that makes a consistent estimation of the local gradient by solving an adaptive filtering problem with different designs of filters.

Framework to model two stage stochastic unit commitment optimization problems.

PyTorch implementation for " Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference" (https://arxiv.org/abs/1810.02555).

Introduction to options pricing theory and advanced numerical methods for pricing both vanilla and exotic options.

Chance-constrained control and pricing for natural gas networks using Julia/JuMP.

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction

Stochastic Simulation and Statistics in Tidyverse

Numerical integration of SDEs with variance reduction methods for Monte Carlo simulation

An R Library published on CRAN for variance reduction algorithms.