Bayesian Optimization Interface for laplace-torch

Installation

Install PyTorch first, then:

pip install --upgrade laplace-bayesopt

Usage

Basic usage

from laplace_bayesopt.botorch import LaplaceBoTorch

def get_net():
    # Return a *freshly-initialized* PyTorch model
    return torch.nn.Sequential(
        ...
    )

# Initial X, Y pairs, e.g. obtained via random search
train_X, train_Y = ..., ...

model = LaplaceBoTorch(get_net, train_X, train_Y)

# Use this model in your existing BoTorch loop, e.g. to replace BoTorch's SingleTaskGP model.

The full arguments of LaplaceBoTorch can be found in the class documentation.

Check out examples in examples/.

Useful References

• General Laplace approximation: https://arxiv.org/abs/2106.14806
• Laplace for Bayesian optimization: https://arxiv.org/abs/2304.08309
• Benchmark of neural-net-based Bayesian optimizers: https://arxiv.org/abs/2305.20028
• The case for neural networks for Bayesian optimization: https://arxiv.org/abs/2104.11667

Citation

@inproceedings{kristiadi2023promises,
  title={Promises and Pitfalls of the Linearized {L}aplace in {B}ayesian Optimization},
  author={Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Fortuin, Vincent},
  booktitle={AABI},
  year={2023}
}