[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
"SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity" by Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
This repository contains the source code of the paper Primary-Space Adaptive Control Variates using Piecewise-Polynomial Approximations by Miguel Crespo, Adrian Jarabo, and Adolfo Munoz from ACM Transactions on Graphics.
VILTRUM: Varied Integration Layouts for arbiTRary integrals in a Unified Manner - A C++17 header-only library that provides a set of numerical integration routines
Unbiased Deep Learning based Solvers for parametric PDEs
Learning in Noisy MDP (which is governed by stochastic, exogenous input processes) with input-dependent baseline
Controlled importance-weighted cross-validation
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning (ICML 2022)
This project focuses on applying advanced simulation methods for derivatives pricing. It includes Monte-Carlo, Variance Reduction Techniques, Distribution Sampling Methods, Euler Schemes, and Milstein Schemes.
A pytorch-version implementation of RL algorithms. Now it collects TRPO, ClipPPO, A2C, GAIL and ADCV.
Vrednovanje azijskih opcija
Implementation of CUPED/CUPAC
Control variates for variance reduction of MCMC estimates
Project on using control variates for bayesian neural networks
University Project: simulation techniques to price derivatives. It will involve Monte-Carlo, variance-reduction techniques, and advanced simulation methods.
This project provide a practical Python implementation of the CV enhanced CRR binomial tree pricing algorithm proposed on the publication C Chiu, T Dai, Y Lyuu, L Liu, Y Chen, *Option pricing with the control variate technique beyond Monte Carlo simulation*, The North American Journal of Economics and Finance, Volume 62, 2022, Article 101772,
Importance sampling with control variates on top of Distributions.jl
Explanation of variance reduction methods including Common Random Numbers, Antithetic Variates and Control Variates using examples created with R code and simulation software (ARENA).
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