Laplace approximations for Deep Learning.

Statistical Rethinking (2nd ed.) with NumPyro

Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.

Official Code: Estimating Model Uncertainty of Neural Networks in Sparse Information Form, ICML2020.

Bayesian Low-Rank Adaptation for Large Language Models

Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')

Discrete Bayesian optimization with LLMs, PEFT finetuning methods, and the Laplace approximation.

Fit and compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.

Approximate integrals through second-order Taylor expansions

Notebooks for Advanced Statistical Inference(ASI) course at EURECOM

PyTorch implementation of Sparse Function-space Representation of Neural Networks

Fit and evaluate nonlinear regression models.

Base R Implementation of Logistic Regression from Scratch with Regularization, Laplace Approximation and more

Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression

Code accompanying ICLR 2024 paper "Function-space Parameterization of Neural Networks for Sequential Learning"

Laplace approximation of the marginal likelihood

We provide two notebooks that enable users to explore and experiment with some BDL techniques as Ensembles, MC Dropout and Laplace Approximation. In this way, they allow you to intuitively visualize the main differences among them in a Simulated Dataset and Boston Dataset.

AutoDiff-Inference: Automatic Differentiation Inference. This Repository combines ADVI's change of variable power with Laplace Approximation to provide better inference for constrained parameters. Work done as a part of development of Bijax

🤔 Methods for measuring and visualising the uncertainty in neural networks