Meta-Learning

Probabilistic Matrix Factorization for Automated Machine Learning NeurIPS 2018 Probabilistic Model-Agnostic Meta-Learning NeurIPS 2018 Recasting Gradient-Based Meta-Learning as Hierarchical Bayes ICLR 2018 Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior NeurIPS 2018 Reinforced Continual Learning NeurIPS 2018 Reinforcement Learning Algorithm Selection ICLR 2018 Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes AISTATS 2018 SMASH: One-Shot Model Architecture Search through HyperNetworks ICLR 2018 Synthesized Policies for Transfer and Adaptation across Tasks and Environments NeurIPS 2018 The Importance of Sampling inMeta-Reinforcement Learning NeurIPS 2018 Transfer Learning via Learning to Transfer ICML 2018 Transfer Learning with Neural AutoML NeurIPS 2018 Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities RSS 2017 AdaNet: Adaptive Structural Learning of Artificial Neural Networks ICML 2017 A Meta-Learning Perspective on Cold-Start Recommendations for Items NeurIPS 2017 An Empirical Bayes Approach to Optimizing Machine Learning Algorithms NeurIPS 2017 Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning IJCAI 2017 Bayesian Optimization with Tree-structured Dependencies ICML 2017 Discovering and Exploiting Additive Structure for Bayesian Optimization AISTATS 2017 Discretized Continuous Speech Emotion Recognition with Multi-Task Deep Recurrent Neural Network Interspeech 2017 Fast Video Classification via Adaptive Cascading of Deep Models CVPR 2017 From Bayesian Sparsity to Gated Recurrent Nets NeurIPS 2017 Learning Algorithms for Active Learning ICML 2017 Learning Scalable Deep Kernels with Recurrent Structure JMLR 2017 Learning to Learn From Noisy Web Videos CVPR 2017