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Bayesian Optimization

221 papers

Papers per year

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7
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Papers

Semi-Parametric Sampling for Stochastic Bandits with Many Arms AAAI 2019
Striking the Right Balance With Uncertainty CVPR 2019
No-Regret Bayesian Optimization with Unknown Hyperparameters JMLR 2019
Bayesian Optimization for Policy Search via Online-Offline Experimentation JMLR 2019
Scalable Hyperparameter Transfer Learning NIPS 2018
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs NIPS 2018
Automating Bayesian optimization with Bayesian optimization NIPS 2018
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior NIPS 2018
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation NIPS 2018
Efficient nonmyopic batch active search NIPS 2018
Optimistic optimization of a Brownian NIPS 2018
Probabilistic Matrix Factorization for Automated Machine Learning NIPS 2018
Scaling Gaussian Process Regression with Derivatives NIPS 2018
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation NIPS 2018
Bayesian Model-Agnostic Meta-Learning NIPS 2018
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning ICML 2018
Bayesian Optimization of Combinatorial Structures ICML 2018
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design ICML 2018
Optimization, fast and slow: optimally switching between local and Bayesian optimization ICML 2018
Stagewise Safe Bayesian Optimization with Gaussian Processes ICML 2018
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NIPS 2017
Improving the Expected Improvement Algorithm NIPS 2017
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms NIPS 2017
Multi-Information Source Optimization NIPS 2017
Deep Kernels for Optimizing Locomotion Controllers CORL 2017