Chaospy - Toolbox for performing uncertainty quantification.

Providing reproducibility in deep learning frameworks

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

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

Riemannian stochastic optimization algorithms: Version 1.0.3

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

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

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)

A platform for distributed optimization expriments using OpenMPI

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Framework to model two stage stochastic unit commitment optimization problems.

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.

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

Variance reduction in energy estimators accelerates the exponential convergence in deep learning (ICLR'21)

Code the ICML 2024 paper: "Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models"

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

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

Command line tool to generate mesh-based weight windows. Supports all MCNPv6.2 legacy meshtal output formats for rectangular and cylindrical meshes.

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS, 2022

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