hyperparameter optimization

No Regret Bound for Extreme Bandits AISTATS 2016 Pareto Frontier Learning with Expensive Correlated Objectives ICML 2016 Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters ICML 2016 SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques NeurIPS 2016 Towards Automatically-Tuned Neural Networks AutoML 2016 Efficient and Robust Automated Machine Learning NeurIPS 2015 Gradient-based Hyperparameter Optimization through Reversible Learning ICML 2015 pyGPs -- A Python Library for Gaussian Process Regression and Classification JMLR 2015 Scalable Bayesian Optimization Using Deep Neural Networks ICML 2015 Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains ICML 2015 Automated Variational Inference for Gaussian Process Models NeurIPS 2014 Input Warping for Bayesian Optimization of Non-Stationary Functions ICML 2014 Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images CVPR 2014 Robust and Efficient Kernel Hyperparameter Paths with Guarantees ICML 2014 Efficient Optimization for Sparse Gaussian Process Regression NeurIPS 2013 Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures ICML 2013 Multi-Task Bayesian Optimization NeurIPS 2013 Dual-Space Analysis of the Sparse Linear Model NeurIPS 2012 Practical Bayesian Optimization of Machine Learning Algorithms NeurIPS 2012 Random Search for Hyper-Parameter Optimization JMLR 2012 Algorithms for Hyper-Parameter Optimization NeurIPS 2011 Gaussian Process Training with Input Noise NeurIPS 2011 Robust Gaussian Process Regression with a Student- Likelihood JMLR 2011 A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design JMLR 2010 Descent Methods for Tuning Parameter Refinement AISTATS 2010