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Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools.
Multivariate binary discrimination by the kernel method
Aitchison, J. and Aitken, C. (1976) · 1976
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Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
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Bayesian variational optimization for combinatorial spaces
Wu, T., Flam-Shepherd, D., and Aspuru-Guzik, A. (2020) · 2011
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Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. (2012) · 2012
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Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, J., Brent, K., Chris, E., Dan, Y., and David, D. (2015) · 2015
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Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R., and Freitas, N. D. (2015) · 2015
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The CMA evolution strategy: A tutorial
Hansen, N. (2016) · 2016
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CMA-ES for hyperparameter optimization of deep neural networks
Loshchilov, I. and Hutter, F. (2016) · 2016
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Bayesian optimization of combinatorial structures
Baptista, R. and Poloczek, M. (2018) · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F. (2018) · 2018
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2018) · 2018
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Tune: A research platform for distributed model selection and training
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J., and Stoica, I. (2018) · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019) · 2019
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Combinatorial Bayesian optimization using the graph cartesian product
Oh, C., Tomczak, J., Gavves, E., and Welling, M. (2019) · 2019
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Multiobjective tree-structured Parzen estimator for computationally expensive optimization problems
Ozaki, Y., Tanigaki, Y., Watanabe, S., and Onishi, M. (2020) · 2020
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On the importance of domain model configuration for automated planning engines
Vallati, M., Chrpa, L., McCluskey, T., and Hutter, F. (2021) · 2021
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On the importance of hyperparameter optimization for model-based reinforcement learning
Zhang, B., Rajan, R., Pineda, L., Lambert, N., Biedenkapp, A., Chua, K., Hutter, F., and Calandra, R. (2021) · 2021
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Tensor Programs V: Tuning large neural networks via zero-shot hyperparameter transfer
Yang, G., Hu, E., Babuschkin, I., Sidor, S., Liu, X., Farhi, D., Ryder, N., Pachocki, J., Chen, W., and Gao, J. (2022) · 2022
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Bayesian optimization over high-dimensional combinatorial spaces via dictionary-based embeddings
Deshwal, A., Ament, S., Balandat, M., Bakshy, E., Doppa, J., and Eriksson, D. (2023) · 2023
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Bayesian optimization
Garnett, R. (2023) · 2023
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Toward real-world automated antibody design with combinatorial Bayesian optimization
Khan, A., Cowen-Rivers, A., Grosnit, A., Robert, P., Greiff, V., Smorodina, E., Rawat, P., Akbar, R., Dreczkowski, K., Tutunov, R., et al. (2023) · 2023
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Bounce: Reliable high-dimensional Bayesian optimization for combinatorial and mixed spaces
Papenmeier, L., Nardi, L., and Poloczek, M. (2023) · 2023
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Happywhale - whale and dolphin identification
Cheeseman, T., Southerland, K., Reade, W., and Howard, A. (2022) · 2022
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Multiobjective tree-structured Parzen estimator
Ozaki, Y., Tanigaki, Y., Watanabe, S., Nomura, M., and Onishi, M. (2022) · 2022
Cited alongside, same era.
On the importance of architectures and hyperparameters for fairness in face recognition
Sukthanker, R., Dooley, S., Dickerson, J., White, C., Hutter, F., and Goldblum, M. (2022) · 2022
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On the importance of hyperparameters and data augmentation for self-supervised learning
Wagner, D., Ferreira, F., Stoll, D., Schirrmeister, R., Müller, S., and Hutter, F. (2022) · 2022
Cited alongside, same era.
Multi-objective tree-structured Parzen estimator meets meta-learning
Watanabe, S., Awad, N., Onishi, M., and Hutter, F. (2022) · 2022
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Watanabe, S. and Hutter, F. (2022) · 2022
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Python wrapper for simulating multi-fidelity optimization on HPO benchmarks without any wait
Watanabe, S. (2023a)
Cited in the paper.
A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species
Patton, P., Cheeseman, T., Abe, K., Yamaguchi, T., Reade, W., Southerland, K., Howard, A., Oleson, E., Allen, J., Ashe, E., et al. (2023) · 2023
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Speeding up multi-objective hyperparameter optimization by task similarity-based meta-learning for the tree-structured Parzen estimator
Watanabe, S., Awad, N., Onishi, M., and Hutter, F. (2023) · 2023
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c-TPE: tree-structured Parzen estimator with inequality constraints for expensive hyperparameter optimization
Watanabe, S. and Hutter, F. (2023) · 2023
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Framework and benchmarks for combinatorial and mixed-variable Bayesian optimization
Dreczkowski, K., Grosnit, A., and Ammar, H. B. (2024) · 2024
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Multi-fidelity methods for optimization: A survey
Li, K. and Li, F. (2024) · 2024
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Fast benchmarking of asynchronous multi-fidelity optimization on zero-cost benchmarks
Watanabe, S., Mallik, N., Bergman, E., and Hutter, F. (2024) · 2024
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