NIPS Proceedingsβ

Mark van der Wilk

6 Papers

  • Bayesian Layers: A Module for Neural Network Uncertainty (2019)
  • Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes (2019)
  • Learning Invariances using the Marginal Likelihood (2018)
  • Convolutional Gaussian Processes (2017)
  • Understanding Probabilistic Sparse Gaussian Process Approximations (2016)
  • Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models (2014)