hyperparameter tuning

AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI AISTATS 2019 Bayesian Optimization for Policy Search via Online-Offline Experimentation JMLR 2019 Bayesian Optimization with Binary Auxiliary Information UAI 2019 Bayesian Optimization with Unknown Search Space NeurIPS 2019 Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence NeurIPS 2019 Fast Efficient Hyperparameter Tuning for Policy Gradient Methods NeurIPS 2019 Flambé: A Customizable Framework for Machine Learning Experiments ACL 2019 Hyper-parameter Tuning under a Budget Constraint IJCAI 2019 Improving Model Selection by Employing the Test Data ICML 2019 Learning Optimal Linear Regularizers ICML 2019 Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion AAAI 2019 Multivariate Time Series Prediction Based on Optimized Temporal Convolutional Networks with Stacked Auto-encoders ACML 2019 No-Regret Bayesian Optimization with Unknown Hyperparameters JMLR 2019 Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning UAI 2019 Safe Grid Search with Optimal Complexity ICML 2019 Truncated Back-propagation for Bilevel Optimization AISTATS 2019 Tunability: Importance of Hyperparameters of Machine Learning Algorithms JMLR 2019 Exploiting Strategy-Space Diversity for Batch Bayesian Optimization AISTATS 2018 Learning in Reproducing Kernel Kreı̆n Spaces ICML 2018 Multi-Fidelity Black-Box Optimization with Hierarchical Partitions ICML 2018 Neural Architecture Search with Bayesian Optimisation and Optimal Transport NeurIPS 2018 On the State of the Art of Evaluation in Neural Language Models ICLR 2018 Probabilistic Matrix Factorization for Automated Machine Learning NeurIPS 2018 Regularization Learning Networks: Deep Learning for Tabular Datasets NeurIPS 2018 Towards Automated Single Channel Source Separation Using Neural Networks Interspeech 2018