ae-foster / dad
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
☆30Updated 3 years ago
Alternatives and similar repositories for dad:
Users that are interested in dad are comparing it to the libraries listed below
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆21Updated 4 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- ☆15Updated 2 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Bayesian active learning with EPIG data acquisition☆28Updated last month
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated 9 months ago
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆93Updated 3 years ago
- Gaussian Process and Uncertainty Quantification Summer School 2020☆33Updated 2 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆70Updated 4 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Benchmark functions for Bayesian optimization☆33Updated last year
- A community repository for benchmarking Bayesian methods☆11Updated last year
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- Bayesian Neural Network Surrogates for Bayesian Optimization☆48Updated 10 months ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆41Updated 7 months ago
- ☆22Updated last year
- Minimal Gaussian process library in JAX with a simple (custom) approach to state management.☆11Updated last year
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 4 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆82Updated 4 years ago
- Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)☆13Updated this week
- Code for our paper: Online Variational Filtering and Parameter Learning☆18Updated 3 years ago
- Code for the paper: "Independent mechanism analysis, a new concept?"☆24Updated last year
- Implementation of the Gaussian Process Autoregressive Regression Model☆62Updated 2 months ago
- ☆53Updated 8 months ago
- Natural Gradient, Variational Inference☆29Updated 5 years ago
- Deep universal probabilistic programming with Python and PyTorch☆11Updated 4 years ago