A platform for distributed optimization expriments using OpenMPI

SAGA with Perturbations

Machine Learning

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

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

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

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

Simple MATLAB toolbox for deep learning network: Version 1.0.3

An R Library published on CRAN for variance reduction algorithms.

Stochastic Simulation and Statistics in Tidyverse

[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution

Project on using control variates for bayesian neural networks

Framework to model two stage stochastic unit commitment optimization problems.

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

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.

Reproduced PyTorch implementation for ICML 2017 Paper "Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning."

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

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

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.