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 60 articles )

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  1. Ahmed, Mohamed Osama; Vaswani, Sharan; Schmidt, Mark: Combining Bayesian optimization and Lipschitz optimization (2020)
  2. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  3. Beyhaghi, Pooriya; Alimo, Ryan; Bewley, Thomas: A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging (2020)
  4. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  5. Jiang, Wei; Siddiqui, Sauleh: Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (2020)
  6. 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)
  7. 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)
  8. Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em: Physics-informed neural networks for high-speed flows (2020)
  9. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  10. Sambasivan, Rajiv; Das, Sourish; Sahu, Sujit K.: A Bayesian perspective of statistical machine learning for big data (2020)
  11. Sirén, Jukka; Kaski, Samuel: Local dimension reduction of summary statistics for likelihood-free inference (2020)
  12. 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)
  13. Wu, Hao; Noé, Frank: Variational approach for learning Markov processes from time series data (2020)
  14. Ariafar, Setareh; Coll-Font, Jaume; Brooks, Dana; Dy, Jennifer: ADMMBO: Bayesian optimization with unknown constraints using ADMM (2019)
  15. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  16. Candelieri, Antonio; Giordani, Ilaria; Archetti, Francesco; Barkalov, Konstantin; Meyerov, Iosif; Polovinkin, Alexey; Sysoyev, Alexander; Zolotykh, Nikolai: Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization (2019)
  17. ChangYong Oh, Efstratios Gavves, Max Welling: BOCK : Bayesian Optimization with Cylindrical Kernels (2019) arXiv
  18. Flaxman, Seth; Chirico, Michael; Pereira, Pau; Loeffler, Charles: Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ “Real-time crime forecasting challenge” (2019)
  19. Järvenpää, Marko; Gutmann, Michael U.; Pleska, Arijus; Vehtari, Aki; Marttinen, Pekka: Efficient acquisition rules for model-based approximate Bayesian computation (2019)
  20. Joy, Thomas; Desmaison, Alban; Ajanthan, Thalaiyasingam; Bunel, Rudy; Salzmann, Mathieu; Kohli, Pushmeet; Torr, Philip H. S.; Kumar, M. Pawan: Efficient relaxations for dense CRFs with sparse higher-order potentials (2019)

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