Statistical Machine Learning Project on Variational Inference
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Updated
Nov 4, 2020 - Jupyter Notebook
Statistical Machine Learning Project on Variational Inference
Implementation of Sparse Information Filter for Fast Gaussian Process Regression Kania et al. (2021) (https://link.springer.com/chapter/10.1007/978-3-030-86523-8_32)
VSPsnap is a collection of R and Python code for Gaussian Process regression in a kriging-like setting (i.e. two features (X,Y) and a target (Z)) - with a focus on SARS-CoV2 data (genomic/IR/FR).
Investigative project for my CST Part III Probabilistic Machine Learning (LE49) module
Sampling from stochastic processes in Python.
This repository presents our research on optimizing crutch designs using Gaussian Processes (GPs) and Bayesian Optimization (BO). We introduce a novel loss function that blends subjective (pain, instability, effort) and objective measures, leading to a personalized, more efficient, and comfortable crutch design.
A multi-target regression algorithm based on Gaussian process regression
Projet ENSAE : Optimisation bayésienne d'algorithmes de Machine Learning
squidward is a package for gaussian process modeling
Gauss process integrated with forward automatic differentiation
An optimal experimental design framework for accelerating knowledge discovery using gene expression data
Code to compute the relevance of a set of features for a regression task
A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)
Machine Learning Projects on Linear Regression, Classification and Multi-layer Perceptrons, and Gaussian Processes and SVMs (October, 2019).
Stores a bank of Gaussian process models that I can reuse/adapt as I need
Group project for my CST Part III Machine Learning and the Physical World (L48) module
Robust Conjugate Direction Search (noisy objective optimization)
Small set of stats examples for fun!
Probabilistic ML
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