SAGA with Perturbations

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

My Master's Thesis on Variational Optimization of Neural Networks written at the Technical University of Denmark

Project on using control variates for bayesian neural networks

Statistical toolkit to make time-series stationary

Machine Learning

Training a single layer perceptron model on sparse data (coursework)

Importance sampling in R course notes and code

We consider a problem of minimizing a sum of two functions and propose a generic algorithmic framework (SAE) to separate oracle complexities for each function. We compare the performance of splitting accelerated enveloped accelerated variance reduced method with a different sliding technique.

Antithetic Variates for Monte Carlo Variance Reduction

An R Library published on CRAN for variance reduction algorithms.

Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen

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.

Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates

EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS, 2022

IOE 574 - Simulation Design & Analysis; Term project code & documentation

Stochastic Simulation and Statistics in Tidyverse

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