Talk Slides
- Foundations of differentiable programming in probabilistic models
- Sampling with mirrored Stein operators
- Neural networks as inter-domain inducing points
- Function-space orthogonality in probabilistic learning
- Sparse orthogonal variational inference for Gaussian processes
- Inference networks for Gaussian processes
- A spectral approach to gradient estimation for implicit distributions