papers.md

id	title
papers	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:

• Advancing Bayesian Optimization via Learning Correlated Latent Space. Seunghun Lee, Jaewon Chu, Sihyeon Kim, Juyeon Ko, Hyunwoo J. Kim. NeurIPS 2023.

• Unexpected Improvements to Expected Improvement for Bayesian Optimization. Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy. NeurIPS 2023.

• Self-Correcting Bayesian Optimization through Bayesian Active Learning. Carl Hvarfner, Erik Orm Hellsten, Frank Hutter, Luigi Nardi. NeurIPS 2023.

• Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. Sam Daulton, Max Balandat, Eytan Bakshy. ICML 2023.

• Global Optimization with Parametric Function Approximation. Chong Liu, Yu-Xiang Wang. ICML 2023.

• Sparse Bayesian Optimization. Sulin Liu, Qing Feng, David Eriksson, Ben Letham, Eytan Bakshy. AISTATS 2023.

• Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson. AISTATS 2023.

• qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy, Peter I. Frazier. AISTATS 2023.

• Discovering Many Diverse Solutions with Bayesian Optimization. Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner. AISTATS 2023.

• Multi-Fidelity Bayesian Optimization with Unreliable Information Sources. Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski. AISTATS 2023.

• Bayesian Optimization with Conformal Coverage Guarantees. Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson. AISTATS 2023.

• Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation. Henry B. Moss, Sebastian W. Ober, Victor Picheny. AISTATS 2023.

• Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. Felix Jimenez, Matthias Katzfuss. AISTATS 2023.

• Active Bayesian Causal Inference. Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen. NeurIPS 2022.

• Joint Entropy Search for Multi-objective Bayesian Optimization. Ben Tu, Axel Gandy, Nikolas Kantas, Behrang Shafei. NeurIPS 2022.

• Local Bayesian Optimization via Maximizing Probability of Descent. Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett. NeurIPS 2022.

• Local Latent Space Bayesian Optimization over Structured Inputs. Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob Gardner. NeurIPS 2022.

• SnAKe: Bayesian Optimization via Pathwise Exploration. Jose Pablo Folch, Shiqiang Zhang, Robert Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener. NeurIPS 2022.

• Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A Osborne, Eytan Bakshy. NeurIPS 2022.

• Rethinking Optimization with Differentiable Simulation from a Global Perspective. Rika Antonova, Jingyun Yang, Krishna Murthy Jatavallabhula, Jeannette Bohg. CoRL 2022.

• Robust Multi-Objective Bayesian Optimization Under Input Noise. Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy. ICML 2022.

• Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders . Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson. ICML 2022.

• Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy. UAI 2022.

• Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. Jerry Lin, Raul Astudillo, Peter Frazier, Eytan Bakshy. AISTATS 2022.

• Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. Benjamin Letham, Eytan Bakshy, Michael Shvartsman. AISTATS 2022.

• GIBBON: General-purpose Information-Based Bayesian OptimisatioN. Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson. JMLR 2021.

• Bayesian Optimization of Function Networks . Raul Astudillo, Peter Frazier. NeurIPS 2021.

• Conditioning Sparse Variational Gaussian Processes for Online Decision-making. Wesley J. Maddox, Samuel Stanton, and Andrew G. Wilson. NeurIPS 2021.

• Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier. NeurIPS 2021.

• Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. Samuel Daulton, Max Balandat, Eytan Bakshy. NeurIPS 2021.

• Bayesian Optimization with High-Dimensional Outputs. Wesley J. Maddox, Maximilian Balandat, Andrew G. Wilson, Eytan Bakshy. NeurIPS 2021.

• Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces. Aryan Deshwal, Jana Doppa. NeurIPS 2021.

• Improving Black-box Optimization in VAE Latent Space Using Decoder Uncertainty. Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal. NeurIPS 2021.

• Local Policy Search with Bayesian Optimization. Sarah Müller, Alexander von Rohr, Sebastian Trimpe. NeurIPS 2021.

• Risk-averse Heteroscedastic Bayesian Optimization. Anastasia Makarova, Ilnura Usmanova, Ilija Bogunovic, Andreas Krause. NeurIPS 2021.

• 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.

• Bayesian Optimization of Risk Measures. Sait Cakmak, Raul Astudillo Marban, Peter Frazier, Enlu Zhou. NeurIPS 2020.

• Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. Sam Daulton, Maximilian Balandat, Eytan Bakshy. 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.

• High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds. Noémie Jaquier, Leonel Rozo. NeurIPS 2020.

• High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy. 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.

Please feel free to add any other peer reviewed works that build off of botorch via a PR!