| Structured Variational Inference in Continuous Cox Process Models | Jun 7, 2019 | Numerical IntegrationUncertainty Quantification | CodeCode Available | 0 |
| Deep Compositional Spatial Models | Jun 6, 2019 | Gaussian ProcessesUncertainty Quantification | —Unverified | 0 |
| Bayesian Evidential Deep Learning with PAC Regularization | Jun 3, 2019 | Deep LearningUncertainty Quantification | CodeCode Available | 0 |
| Generative Parameter Sampler For Scalable Uncertainty Quantification | May 28, 2019 | General ClassificationUncertainty Quantification | —Unverified | 0 |
| Distributed estimation of the inverse Hessian by determinantal averaging | May 28, 2019 | Distributed OptimizationUncertainty Quantification | —Unverified | 0 |
| Sequential Gaussian Processes for Online Learning of Nonstationary Functions | May 24, 2019 | Gaussian ProcessesHyperparameter Optimization | CodeCode Available | 0 |
| A Bulirsch-Stoer algorithm using Gaussian processes | May 23, 2019 | Gaussian ProcessesGPR | —Unverified | 0 |
| Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models | May 20, 2019 | Uncertainty QuantificationVariational Inference | —Unverified | 0 |
| Minimal Achievable Sufficient Statistic Learning | May 19, 2019 | BIG-bench Machine LearningUncertainty Quantification | CodeCode Available | 0 |
| Efficient Deep Gaussian Process Models for Variable-Sized Input | May 16, 2019 | Gaussian ProcessesUncertainty Quantification | CodeCode Available | 0 |
