Papers using BoTorch

The main reference for BoTorch is

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization:

@inproceedings{balandat2020botorch,
    title = {{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}},
    author = {Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan},
    booktitle = {Advances in Neural Information Processing Systems 33},
    year = 2020,
    url = {https://proceedings.neurips.cc/paper/2020/hash/f5b1b89d98b7286673128a5fb112cb9a-Abstract.html}
}

Here is an incomplete selection of peer-reviewed Bayesian optimization papers that build off of BoTorch:

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. Sam Daulton, Maximilian Balandat, Eytan Bakshy. NeurIPS 2020.
Bayesian Optimization of Risk Measures . Sait Cakmak, Raul Astudillo Marban, Peter Frazier, Enlu Zhou. NeurIPS 2020.
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization . Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy. NeurIPS 2020.
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds. Noémie Jaquier, Leonel Rozo. NeurIPS 2020.
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. Shali Jiang, Daniel Jiang, Maximilian Balandat, Brian Karrer, Jacob Gardner, Roman Garnett. NeurIPS 2020.
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy. NeurIPS 2020.
PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks . Ting-Wu Chin, Ari S. Morcos, Diana Marculescu. ICML 2020 Workshop on Real World Experiment Design and Active Learning.