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  year={2019},
}

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  year={2020},
}

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}

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}

@article{ghoreishi2019multi,
  title={{Multi-information source constrained Bayesian optimization}},
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}

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  title = 	 {{ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations}},
  author =       {Korovina, Ksenia and Xu, Sailun and Kandasamy, Kirthevasan and Neiswanger, Willie and Poczos, Barnabas and Schneider, Jeff and Xing, Eric},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = 	 {2020},
}

@article{zhang2020bayesian,
  title={{Bayesian optimization for materials design with mixed quantitative and qualitative variables}},
  author={Zhang, Yichi and Apley, Daniel W and Chen, Wei},
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  pages={1--13},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{huang2006sequential,
  title={Sequential kriging optimization using multiple-fidelity evaluations},
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}

@inproceedings{zhang2017information,
  title={{Information-based multi-fidelity Bayesian optimization}},
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  booktitle={NeurIPS Workshop on Bayesian Optimization},
  year={2017}
}

@inproceedings{li2020multi,
  title={{Multi-fidelity Bayesian optimization via deep neural networks}},
  author={Li, Shibo and Xing, Wei and Kirby, Robert and Zhe, Shandian},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  pages={8521--8531},
  year={2020}
}

@inproceedings{sen2018multi,
  title={Multi-fidelity black-box optimization with hierarchical partitions},
  author={Sen, Rajat and Kandasamy, Kirthevasan and Shakkottai, Sanjay},
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  pages={4538--4547},
  year={2018},
}

@article{bombarelli2018lbo,
	year = 2018,
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	author = {Rafael G{\'{o}}mez-Bombarelli and Jennifer N. Wei and David Duvenaud and Jos{\'{e}} Miguel Hern{\'{a}}ndez-Lobato and Benjam{\'{\i}}n S{\'{a}}nchez-Lengeling and Dennis Sheberla and Jorge Aguilera-Iparraguirre and Timothy D. Hirzel and Ryan P. Adams and Al{\'{a}}n Aspuru-Guzik},
	title = {{Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules}},
	journal = {{ACS} Central Science}
}

@article{moss21gibbon,
  author  = {Henry B. Moss and David S. Leslie and Javier Gonz{\'{a}}lez and Paul Rayson},
  title   = {{GIBBON: General-purpose Information-Based Bayesian Optimisation}},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  pages   = {1--49},
  url     = {http://jmlr.org/papers/v22/21-0120.html}
}

@inproceedings{bogunovic2018adversarially,
  title={{Adversarially robust optimization with Gaussian processes}},
  author={Bogunovic, Ilija and Scarlett, Jonathan and Jegelka, Stefanie and Cevher, Volkan},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2018}
}

@inproceedings{balandat2020botorch,
 author = {Balandat, Maximilian and Karrer, Brian and Jiang, Daniel and Daulton, Samuel and Letham, Ben and Wilson, Andrew G and Bakshy, Eytan},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 title = {{BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization}},
 year = {2020}
}

@inproceedings{gardner2018gpytorch,
  title={{GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration}},
  author={Gardner, Jacob and Pleiss, Geoff and Weinberger, Kilian Q and Bindel, David and Wilson, Andrew G},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year={2018}
}

@inproceedings{wang2017max,
  title={{Max-value entropy search for efficient Bayesian optimization}},
  author={Wang, Zi and Jegelka, Stefanie},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={3627--3635},
  year={2017},
  organization={PMLR}
}

@InProceedings{aglietti_2023_ceo,  
  author = {Branchini, Nicola and Aglietti, Virginia and Dhir, Neil and Damoulas, Theodoros},
  title = {Causal Entropy Optimization},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},  
  year = {2023}
}

@inproceedings{bogunovic_2020_drbo,
  author={Kirschner, Johannes and Bogunovic, Ilija and Jegelka, Stefanie and Krause, Andreas},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
  title = {{Distributionally Robust Bayesian Optimization}},
  year = {2020}
}

@inproceedings{eriksson_2023_sparsebo,
  author = {Liu, Sulin and Feng, Qing and Eriksson, David and Letham, Benjamin and Bakshy, Eytan},
  title = {{Sparse Bayesian Optimization}},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = {2023},
}

@InProceedings{eriksson_21_saasbo,
  title = {High-dimensional {Bayesian} optimization with sparse axis-aligned subspaces},
  author = {Eriksson, David and Jankowiak, Martin},
  booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = 	 {2021}
}

@article{Piironen_2017_horseshoe,
	doi = {10.1214/17-ejs1337si},
  
	url = {https://doi.org/10.1214%2F17-ejs1337si},
  
	year = 2017,
	month = {jan},
  
	publisher = {Institute of Mathematical Statistics},
  
	volume = {11},
  
	number = {2},
  
	author = {Juho Piironen and Aki Vehtari},
  
	title = {Sparsity information and regularization in the horseshoe and other shrinkage priors},
  
	journal = {Electronic Journal of Statistics}
}

@InProceedings{rolland_2018_additivebo,
  title = 	 {{High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups}},
  author = 	 {Rolland, Paul and Scarlett, Jonathan and Bogunovic, Ilija and Cevher, Volkan},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = 	 {2018}
}

@inproceedings{letham_2020_randomprojbo,
 author = {Letham, Ben and Calandra, Roberto and Rai, Akshara and Bakshy, Eytan},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {1546--1558},
 publisher = {Curran Associates, Inc.},
 title = {Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/10fb6cfa4c990d2bad5ddef4f70e8ba2-Paper.pdf},
 volume = {33},
 year = {2020}
}


@InProceedings{paananen_19_postsensi,
  title = {{Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution}},
  author = {Paananen, Topi and Piironen, Juho and Andersen, Michael Riis and Vehtari, Aki},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = 	 {2019},
}


@InProceedings{sebenius_2022_featurecollapse,
  title = 	 {{Feature Collapsing for Gaussian Process Variable Ranking}},
  author =       {Sebenius, Isaac and Paananen, Topi and Vehtari, Aki},
  booktitle = 	 {Proceedings of The International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = 	 {2022},
}

@inproceedings{piironen_16_projpredgp,
	author = {Juho Piironen and Aki Vehtari},
	title = {{Projection predictive model selection for Gaussian processes}},
	booktitle = {Proceeding of the {IEEE} International Workshop on Machine Learning for Signal Processing ({MLSP})},
    year = {2016}
}

@article{zhang_04_lengthscale,
author = {Zhang, Hao},
year = {2004},
month = {02},
pages = {250-261},
title = {Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics},
volume = {99},
journal = {Journal of the American Statistical Association},
}

@inproceedings{daulton_20_ehvi,
 author = {Daulton, Samuel and Balandat, Maximilian and Bakshy, Eytan},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {9851--9864},
 publisher = {Curran Associates, Inc.},
 title = {Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/6fec24eac8f18ed793f5eaad3dd7977c-Paper.pdf},
 volume = {33},
 year = {2020}
}

@inproceedings{ziomek2023random,
      title={{Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?}}, 
      author={Juliusz Ziomek and Haitham Bou-Ammar},
      year={2023},
      booktitle={Proceedings of the International Conference on Machine Learning (ICML)},
}

@inproceedings{li2018high,
      title={{High dimensional Bayesian optimization using dropout}}, 
      author={Cheng Li and Sunil Gupta and Santu Rana and Vu Nguyen and Svetha Venkatesh and Alistair Shilton},
      year={2017},
      booktitle={Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)}
}

@inproceedings{bogunovic_21_missgp,
 author = {Bogunovic, Ilija and Krause, Andreas},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {3004--3015},
 publisher = {Curran Associates, Inc.},
 title = {Misspecified Gaussian Process Bandit Optimization},
 url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/177db6acfe388526a4c7bff88e1feb15-Paper.pdf},
 volume = {34},
 year = {2021}
}

@inproceedings{krauze_11_contextbo,
 author = {Krause, Andreas and Ong, Cheng},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 title = {{Contextual Gaussian Process Bandit Optimization}},
 year = {2011}
}

@inproceedings{eriksson_2021_nonmyopiccost,
    title={{A Nonmyopic Approach to Cost-Constrained Bayesian Optimization}}, 
    author={Eric Hans Lee and David Eriksson and Valerio Perrone and Matthias Seeger},
    booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI)},
    year = {2021}
}

@article{wilson2017reparameterization,
      title={The reparameterization trick for acquisition functions}, 
      author={James T. Wilson and Riccardo Moriconi and Frank Hutter and Marc Peter Deisenroth},
      year={2017},
      journal={arXiv preprint arXiv:1712.00424}
}

@article{shen2021vsbo,
      title={{Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection}}, 
      author={Yihang Shen and Carl Kingsford},
      year={2021},
      journal={arXiv preprint arXiv:2109.09264},
}

@inproceedings{Spagnol2019sensibo,
  title={{Bayesian optimization in effective dimensions via kernel-based sensitivity indices}},
  author={Adrien Spagnol and Rodolphe Le Riche and S{\'e}bastien Da Veiga},
  year={2019},
  booktitle = {Proceedings of the International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP)}
}


@InProceedings{ishibashi23stopping,
  title = {{A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets}},
  author = {Ishibashi, Hideaki and Karasuyama, Masayuki and Takeuchi, Ichiro and Hino, Hideitsu},
  booktitle = {Proceedings of The International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = 2023
}

@article{nega21context,
    author = {Nega, Philip W. and Li, Zhi and Ghosh, Victor and Thapa, Janak and Sun, Shijing and Hartono, Noor Titan Putri and Nellikkal, Mansoor Ani Najeeb and Norquist, Alexander J. and Buonassisi, Tonio and Chan, Emory M. and Schrier, Joshua},
    title = "{Using automated serendipity to discover how trace water promotes and inhibits lead halide perovskite crystal formation}",
    journal = {Applied Physics Letters},
    volume = {119},
    number = {4},
    year = {2021},
}

@article{sobol2001sa,
    title = {Global sensitivity indices for nonlinear mathematical models and their {Monte Carlo} estimates},
    journal = {Mathematics and Computers in Simulation},
    volume = {55},
    number = {1},
    pages = {271-280},
    year = {2001},
    author = {I.M Sobol},
}

@article{Arsenyan2023,
  author    = {Arsenyan, Vahan and Grosnit, Antoine and Bou-Ammar, Haitham},
  title     = {Contextual Causal {B}ayesian Optimisation},
  year      = {2023},
  journal = {arXiv preprint arXiv:2301.12412},
}

@article{crovini2023batch,
      title={{Batch Bayesian Optimization via Particle Gradient Flows}}, 
      author={Enrico Crovini and Simon L. Cotter and Konstantinos Zygalakis and Andrew B. Duncan},
      year={2022},
      journal={arXiv preprint arXiv:2209.04722},
}

@book{Rasmussen2006,
author = {Rasmussen, C. and Williams, C.},
file = {:Users/fxbriol/Library/Application Support/Mendeley Desktop/Downloaded/Rasmussen, Williams - 2006 - Gaussian Processes for Machine Learning.pdf:pdf},
publisher = {MIT Press},
title = {{Gaussian Processes for Machine Learning}},
year = {2006}
}

@article{lee2020costaware,
      title={{Cost-aware Bayesian Optimization}}, 
      author={Eric Hans Lee and Valerio Perrone and Cedric Archambeau and Matthias Seeger},
      year={2020},
      journal={arXiv preprint arXiv:2003.10870},
}

@article{toscano2018bayesian,
author = {Toscano-Palmerin, Saul and Frazier, Peter I.},
title = {Bayesian Optimization with Expensive Integrands},
journal = {SIAM Journal on Optimization},
volume = {32},
number = {2},
pages = {417-444},
year = {2022},
    abstract = { Nonconvex derivative-free time-consuming objectives are often optimized using “black-box” optimization. These approaches assume very little about the objective. While broadly applicable, they typically require more evaluations than methods exploiting more problem structure. Often, such time-consuming objectives are actually the sum or integral of a larger number of functions, each of which consumes significant time when evaluated individually. This arises in designing aircraft, choosing parameters in ride-sharing dispatch systems, and tuning hyperparameters in deep neural networks. We develop a novel Bayesian optimization algorithm that leverages this structure to improve performance. Our algorithm is average-case optimal by construction when a single evaluation of the integrand remains within our evaluation budget. Achieving this one-step optimality requires solving a challenging value of information optimization problem, for which we provide a novel efficient discretization-free computational method. We also prove consistency for our method in both continuum and discrete finite domains for objective functions that are sums. In numerical experiments comparing against previous state-of-the-art methods, including those that also leverage sum or integral structure, our method performs as well or better across a wide range of problems and offers significant improvements when evaluations are noisy or the integrand varies smoothly in the integrated variables. }
}


@article{higgins21materials,
    author = {Higgins, Kate and Ziatdinov, Maxim and Kalinin, Sergei and Ahmadi, Mahshid},
    title = {High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches},
    journal = {Journal of the American Chemical Society},
    volume = {143},
    number = {47},
    year = {2021},

}

@article{Gretton2012JMLR,
author = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Scholkopf, Bernhard and Smola, Alexander},
journal = {Journal of Machine Learning Research},
pages = {723--773},
title = {{A kernel two-sample test}},
volume = {13},
year = {2012}
}

@article{abolhasani23aimaterials,
    author = {Abolhasani, Milad and Brown, Keith A.},
    year = {2023},
    journal = {MRS Bulletin},
    title = {Role of {AI} in experimental materials science},
}

@inproceedings{NEURIPS2020_e8f27796,
 author = {Cakmak, Sait and Astudillo Marban, Raul and Frazier, Peter and Zhou, Enlu},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 pages = {20130--20141},
 title = {{Bayesian Optimization of Risk Measures}},
 year = {2020}
}

@misc{misc_yacht_hydrodynamics_243,
  author       = {Gerritsma,J. and Onnink,R. and Versluis,A.},
  title        = {{Yacht Hydrodynamics}},
  year         = {2013},
  howpublished = {UCI Machine Learning Repository}
}

@misc{amber,
author = {Case, David and Aktulga, H. Metin and Belfon, Kellon and Ben-Shalom, Ido and Berryman, Joshua and Brozell, Scott and Cerutti, David and Cheatham, Thomas and Cisneros, Gerardo Andrés and Cruzeiro, Vinícius and Darden, Tom and Forouzesh, Negin and Giambasu, George and Giese, Timothy and Gilson, Michael and Gohlke, Holger and Götz, Andreas and Harris, Julie and Izadi, Saeed and Kollman, Peter},
year = {2023},
title = {Amber 2023},
doi = {10.13140/RG.2.2.12836.24965}
}

@article{amberarticle,
author = {Salomon Ferrer, Romelia and Case, David and Walker, Ross},
year = {2013},
title = {An overview of the {Amber} biomolecular simulation package},
volume = {3},
journal = {Wiley Interdisciplinary Reviews: Computational Molecular Science},
}

@article{optiamber,
author = {Fang, Lincan and Makkonen, Esko and Todorović, Milica and Rinke, Patrick and Chen, Xi},
year = {2021},
title = {{Efficient Amino Acid Conformer Search with Bayesian Optimization}},
volume = {17},
journal = {Journal of Chemical Theory and Computation}
}

@book{garnett_bayesoptbook_2023,
  author    = {Garnett, Roman},
  title     = {{Bayesian Optimization}},
  year      = {2023},
  publisher = {Cambridge University Press}
}

@article{tulabandhula2014robust,
      title={Robust Optimization using Machine Learning for Uncertainty Sets}, 
      author={Theja Tulabandhula and Cynthia Rudin},
      year={2014},
      eprint={1407.1097},
      archivePrefix={arXiv},
      primaryClass={math.OC},
      journal={arXiv preprint arXiv:1407.1097}
}

@inproceedings{robustsat,
 author = {Artun Saday and Yaşar Cahit Yıldırım and Cem Tekin},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 title = {Robust {Bayesian} Satisficing},
 year = {2023}
}


@inproceedings{husain2023distributionally,
      title={Distributionally Robust {Bayesian} Optimization with $\varphi$-divergences}, 
      author={Hisham Husain and Vu Nguyen and Anton van den Hengel},
      year={2023},
      booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
}

@inproceedings{bocv,
      title={Bayesian Optimization with Cost-varying
Variable Subsets}, 
      author={Sebastian Shenghong Tay and Chuan Sheng Foo and Daisuke Urano and Richalynn Chiu Xian Leong and Bryan Kian Hsiang Low},
      year={2023},
      booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
}


@InProceedings{wangrobot,
  title = 	 {Batched High-dimensional {B}ayesian Optimization via Structural Kernel Learning},
  author =       {Zi Wang and Chengtao Li and Stefanie Jegelka and Pushmeet Kohli},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning (ICML)},
  year = 	 {2017},

}

@inproceedings{grettonnips,
 author = {Gretton, Arthur and Fukumizu, Kenji and Teo, Choon and Song, Le and Sch\"{o}lkopf, Bernhard and Smola, Alex},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {A Kernel Statistical Test of Independence},
 year = {2007}
}

@InProceedings{makarova2022automatic,
  title = 	 {Automatic Termination for Hyperparameter Optimization},
  author =       {Makarova, Anastasia and Shen, Huibin and Perrone, Valerio and Klein, Aaron and Faddoul, Jean Baptiste and Krause, Andreas and Seeger, Matthias and Archambeau, Cedric},
  booktitle = 	 {Proceedings of the First International Conference on Automated Machine Learning},
  year = 	 {2022},
  abstract = 	 {Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning.  At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget, it is hard to pre-specify an optimal value in advance. In this work, we propose an effective and intuitive termination criterion for BO that automatically stops  the procedure if it is sufficiently close to the global optimum. Our key insight is that the discrepancy between the true objective (predictive performance on test data) and the computable target (validation performance) suggests stopping once the suboptimality in optimizing the target is dominated by the statistical estimation error. Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time. Additionally, we find that overfitting may occur in the context of HPO, which is arguably an overlooked problem in the literature, and show how our termination criterion helps to mitigate this phenomenon on both small and large datasets.}
}
