jungtaekkim / bayeso-benchmarks
Benchmark functions for Bayesian optimization
☆33Updated last year
Alternatives and similar repositories for bayeso-benchmarks:
Users that are interested in bayeso-benchmarks are comparing it to the libraries listed below
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization☆41Updated 3 years ago
- Bayesian Optimization with Density-Ratio Estimation☆23Updated 2 years ago
- Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022☆44Updated 2 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆82Updated 4 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- Natural Gradient, Variational Inference☆29Updated 5 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Code for "On the Expressiveness of Approximate Inference in Bayesian Neural Networks"☆13Updated 3 years ago
- A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"☆21Updated 6 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- Implementation of the Functional Neural Process models☆43Updated 4 years ago
- ☆50Updated 8 months ago
- ☆53Updated 8 months ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆31Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'☆25Updated 3 years ago
- ☆15Updated 2 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆43Updated last year
- Simple, but essential Bayesian optimization package☆94Updated 2 months ago
- Pytorch implementation of neural processes and variants☆27Updated 8 months ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Meta-learning Gaussian process (GP) priors via PAC-Bayes bounds☆25Updated last year
- Bayesian Neural Network Surrogates for Bayesian Optimization☆48Updated 10 months ago
- Baselines for Model-Based Optimization☆53Updated 3 years ago
- Bayesian Optimisation over Multiple Continuous and Categorical Inputs (CoCaBO)☆51Updated 5 years ago