Importance sampling with control variates on top of Distributions.jl
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Updated
Mar 16, 2018 - Julia
Importance sampling with control variates on top of Distributions.jl
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.
Vrednovanje azijskih opcija
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.
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning (ICML 2022)
Controlled importance-weighted cross-validation
Learning in Noisy MDP (which is governed by stochastic, exogenous input processes) with input-dependent baseline
Unbiased Deep Learning based Solvers for parametric PDEs
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
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 Muñoz from ACM Transactions on Graphics.
“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
[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
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