Spearmint

Spearmint is a software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus the code name spearmint) in a manner that iteratively adjusts a number of parameters so as to minimize some objective in as few runs as possible.

Keywords for this software

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References in zbMATH (referenced in 72 articles )

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  1. Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
  2. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  3. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  4. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  5. Bachoc, François; Helbert, Céline; Picheny, Victor: Gaussian process optimization with failures: classification and convergence proof (2020)
  6. Beyhaghi, Pooriya; Alimo, Ryan; Bewley, Thomas: A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging (2020)
  7. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  8. Hanafusa, Ryo; Okadome, Takeshi: Bayesian kernel regression for noisy inputs based on Nadaraya-Watson estimator constructed from noiseless training data (2020)
  9. Jiang, Wei; Siddiqui, Sauleh: Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (2020)
  10. Kandasamy, Kirthevasan; Vysyaraju, Karun Raju; Neiswanger, Willie; Paria, Biswajit; Collins, Christopher R.; Schneider, Jeff; Poczos, Barnabas; Xing, Eric P.: Tuning hyperparameters without grad students: scalable and robust Bayesian optimisation with Dragonfly (2020)
  11. Liu, Minliang; Liang, Liang; Sun, Wei: A generic physics-informed neural network-based constitutive model for soft biological tissues (2020)
  12. Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette: Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (2020)
  13. Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em: Physics-informed neural networks for high-speed flows (2020)
  14. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  15. Rontsis, Nikitas; Osborne, Michael A.; Goulart, Paul J.: Distributionally ambiguous optimization for batch Bayesian optimization (2020)
  16. Sambasivan, Rajiv; Das, Sourish; Sahu, Sujit K.: A Bayesian perspective of statistical machine learning for big data (2020)
  17. Sirén, Jukka; Kaski, Samuel: Local dimension reduction of summary statistics for likelihood-free inference (2020)
  18. Wang, Jialei; Clark, Scott C.; Liu, Eric; Frazier, Peter I.: Parallel Bayesian global optimization of expensive functions (2020)
  19. Wang, Qihan; Li, Qingya; Wu, Di; Yu, Yuguo; Tin-Loi, Francis; Ma, Juan; Gao, Wei: Machine learning aided static structural reliability analysis for functionally graded frame structures (2020)
  20. Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus: An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (2020)

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