Code the ICML 2024 paper: "Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models"
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Updated
Jun 25, 2024 - Python
Code the ICML 2024 paper: "Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models"
Chaospy - Toolbox for performing uncertainty quantification.
Providing reproducibility in deep learning frameworks
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)
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS, 2022
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. NeurIPS, 2022
Statistical toolkit to make time-series stationary
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.
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20
Numerical integration of SDEs with variance reduction methods for Monte Carlo simulation
Antithetic Variates for Monte Carlo Variance Reduction
Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen
Importance sampling in R course notes and code
Riemannian stochastic optimization algorithms: Version 1.0.3
Variance reduction in energy estimators accelerates the exponential convergence in deep learning (ICLR'21)
Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates
University Project: simulation techniques to price derivatives. It will involve Monte-Carlo, variance-reduction techniques, and advanced simulation methods.
IOE 574 - Simulation Design & Analysis; Term project code & documentation
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.
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