markm541374 / gpboLinks
gpbo
☆25Updated 4 years ago
Alternatives and similar repositories for gpbo
Users that are interested in gpbo are comparing it to the libraries listed below
Sorting:
- Stochastic Gradient Riemannian Langevin Dynamics☆34Updated 10 years ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 6 years ago
- Neural Processes implementation for 1D regression☆64Updated 6 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆39Updated 6 years ago
- ☆17Updated 6 years ago
- ☆50Updated last year
- Reducing Reparameterization Gradient Variance code.☆33Updated 8 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 5 years ago
- ☆25Updated 7 years ago
- ☆40Updated 6 years ago
- Bayesian optimization in high-dimensions via random embedding.☆115Updated 12 years ago
- Hyperparameter optimization with approximate gradient☆66Updated 4 years ago
- Code for the paper "A Kernel Test of Goodness of Fit" by Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton☆25Updated 9 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆30Updated 5 years ago
- Benchmark suite of test functions suitable for evaluating black-box optimization strategies☆52Updated 4 months ago
- ☆12Updated 2 years ago
- Python package to sample from determinantal point processes☆18Updated 10 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆16Updated 6 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Determinantal Point Processes in Julia☆12Updated 5 years ago
- Non-stationary Off-policy Evaluation☆13Updated 6 years ago
- Variational Fourier Features☆85Updated 4 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆64Updated 7 years ago
- Code for doubly stochastic gradients☆25Updated 10 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper☆51Updated 6 years ago