| Variational Inference with Latent Space Quantization for Adversarial Resilience | Mar 24, 2019 | Quantizationvalid | CodeCode Available | 0 |
| Approximating exponential family models (not single distributions) with a two-network architecture | Mar 18, 2019 | General ClassificationVariational Inference | —Unverified | 0 |
| Deep Gaussian Processes for Multi-fidelity Modeling | Mar 18, 2019 | Bayesian OptimizationDecision Making | CodeCode Available | 0 |
| Combining Model and Parameter Uncertainty in Bayesian Neural Networks | Mar 18, 2019 | Bayesian InferenceModel Selection | CodeCode Available | 0 |
| Learning proposals for sequential importance samplers using reinforced variational inference | Mar 16, 2019 | reinforcement-learningReinforcement Learning | —Unverified | 0 |
| Functional Variational Bayesian Neural Networks | Mar 14, 2019 | Bayesian InferenceGaussian Processes | CodeCode Available | 0 |
| Variational Bayesian Optimal Experimental Design | Mar 13, 2019 | Experimental DesignVariational Inference | CodeCode Available | 0 |
| Elements of Sequential Monte Carlo | Mar 12, 2019 | Bayesian InferenceBIG-bench Machine Learning | —Unverified | 0 |
| Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations | Mar 10, 2019 | Gaussian ProcessesVariational Inference | —Unverified | 0 |
| NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport | Mar 9, 2019 | Variational Inference | —Unverified | 0 |
