University Project: simulation techniques to price derivatives. It will involve Monte-Carlo, variance-reduction techniques, and advanced simulation methods.

Importance sampling with control variates on top of Distributions.jl

Project on using control variates for bayesian neural networks

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.

Vrednovanje azijskih opcija

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

Unbiased Deep Learning based Solvers for parametric PDEs

Learning in Noisy MDP (which is governed by stochastic, exogenous input processes) with input-dependent baseline

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