An R Library published on CRAN for variance reduction algorithms.
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Updated
Dec 6, 2020 - R
An R Library published on CRAN for variance reduction algorithms.
Code the ICML 2024 paper: "Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models"
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
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
Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates
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
Numerical integration of SDEs with variance reduction methods for Monte Carlo simulation
Reproduced PyTorch implementation for ICML 2017 Paper "Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning."
PyTorch implementation for " Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference" (https://arxiv.org/abs/1810.02555).
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
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