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.
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces . David Eriksson, Martin Jankowiak. UAI 2021.
Bayesian Optimization over Permutation Spaces. Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, Dae Hyun Kim. AAAI 2021.
GIBBON: General-purpose Information-Based Bayesian Optimisation. Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson. JMLR, 2021.
Bayesian Optimization with High-Dimensional Outputs. Wesley J. Maddox, Maximilian Balandat, Andrew G. Wilson, Eytan Bakshy. NeurIPS 2021.
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. Samuel Daulton, Maximilian Balandat, Eytan Bakshy. NeurIPS 2021.
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces. Aryan Deshwal, Jana Doppa. NeurIPS 2021.
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier. NeurIPS 2021.
Bayesian Optimization of Function Networks. Raul Astudillo, Peter Frazier. NeurIPS 2021.
Improving black-box optimization in VAE latent space using decoder uncertainty. Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal. NeurIPS 2021
Risk-averse Heteroscedastic Bayesian Optimization. Anastasia Makarova, Ilnura Usmanova, Ilija Bogunovic, Andreas Krause. NeurIPS 2021.