Title |
|---|
 Learning to Learn Kernels with Variational Random FeaturesInternational Conference on Machine Learning (ICML), 2020 |
 Empirical Bayes Transductive Meta-Learning with Synthetic GradientsInternational Conference on Learning Representations (ICLR), 2020 |
 CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through
ContextIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020 |
 Energy-Based Processes for Exchangeable DataInternational Conference on Machine Learning (ICML), 2020 |
 Modeling Continuous Stochastic Processes with Dynamic Normalizing FlowsNeural Information Processing Systems (NeurIPS), 2020 |
 |
 Model Inversion Networks for Model-Based OptimizationNeural Information Processing Systems (NeurIPS), 2019 |
 MetaFun: Meta-Learning with Iterative Functional UpdatesInternational Conference on Machine Learning (ICML), 2019 |
 Convolutional Conditional Neural ProcessesInternational Conference on Learning Representations (ICLR), 2019 |
 Neural Multisensory Scene InferenceNeural Information Processing Systems (NeurIPS), 2019 |
 Omnipush: accurate, diverse, real-world dataset of pushing dynamics with
RGB-D videoIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2019 |
 Sequential Neural ProcessesNeural Information Processing Systems (NeurIPS), 2019 |
 The Functional Neural ProcessNeural Information Processing Systems (NeurIPS), 2019 |
 Fast and Flexible Multi-Task Classification Using Conditional Neural
Adaptive ProcessesNeural Information Processing Systems (NeurIPS), 2019 |
 Noise Contrastive Meta-Learning for Conditional Density Estimation using
Kernel Mean EmbeddingsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 |
 Quantifying Point-Prediction Uncertainty in Neural Networks via Residual
Estimation with an I/O KernelInternational Conference on Learning Representations (ICLR), 2019 |
