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Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes
5 references·4 citing·26 second hop
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes (2019)Dealing with categorical and integer-valu…Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)Taking the Human Out of the L…Scalable Bayesian Optimization Using Deep Neural Networks (2015)Scalable Bayesian Optimizatio…Predictive Entropy Search for Efficient Global Optimization of Black-box Functions (2014)Predictive Entropy Search for…Multi-Task Bayesian Optimization (2013)Multi-Task Bayesian Optimizat…Practical Bayesian Optimization of Machine Learning Algorithms (2012)Practical Bayesian Optimizati…A comparative study of acquisition functions for active learning kriging in reliability-based design optimization (2025)A comparative study of acquis…BO-B&B: A hybrid algorithm based on Bayesian optimization and branch-and-bound for discrete network design problems (2022)BO-B&B: A hybrid algorithm ba…Golem: an algorithm for robust experiment and process optimization (2021)Golem: an algorithm for robus…Bayesian Optimization for Categorical and Category-Specific Continuous Inputs (2020)Bayesian Optimization for Cat…A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise (1964)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Bayesian optimization with robust Bayesian neural networks (2016)Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets (2016)Bayesian Optimization for Accelerating Hyper-Parameter Tuning (2019)A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010)A General Framework for Constrained Bayesian Optimization using Information-based Search (2015)Batched Large-scale Bayesian Optimization in High-dimensional Spaces (2017)Constrained Bayesian Optimization with Noisy Experiments (2018)Design and Analysis of Computer Experiments (1989)Bayesian Optimization (2018)Bayesian Optimization for Adaptive Experimental Design: A Review (2020)Finite-time Analysis of the Multiarmed Bandit Problem (2002)Fairness improvement for black-box classifiers with Gaussian process (2021)Robust optimization – A comprehensive survey (2007)Theory and Applications of Robust Optimization (2010)Network Design and Transportation Planning: Models and Algorithms (1984)Models and algorithms for road network design: a review and some new developments (1998)A review of urban transportation network design problems (2013)Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions (2008)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Reliability-based design optimization using kriging surrogates and subset simulation (2011)Bayesian Optimization with Unknown Constraints (2014)Bayesian Optimization in a Billion Dimensions via Random Embeddings (2016)A Tutorial on Bayesian Optimization (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
this work references cited by second hop

References

  • 2012Practical Bayesian Optimization of Machine Learning Algorithms6,690 cited
  • 2015Taking the Human Out of the Loop: A Review of Bayesian Optimization6,126 cited
  • 2015Scalable Bayesian Optimization Using Deep Neural Networks703 cited
  • 2013Multi-Task Bayesian Optimization484 cited
  • 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functions369 cited

Cited by

  • 2020Bayesian Optimization for Categorical and Category-Specific Continuous Inputs44 cited
  • 2021Golem: an algorithm for robust experiment and process optimization38 cited
  • 2025A comparative study of acquisition functions for active learning kriging in reliability-based design optimization12 cited
  • 2022BO-B&B: A hybrid algorithm based on Bayesian optimization and branch-and-bound for discrete network design problems8 cited

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