NIPS Proceedingsβ

Richard E. Turner

12 Papers

  • Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes (2019)
  • Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model (2019)
  • Practical Deep Learning with Bayesian Principles (2019)
  • Geometrically Coupled Monte Carlo Sampling (2018)
  • Infinite-Horizon Gaussian Processes (2018)
  • Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (2017)
  • Streaming Sparse Gaussian Process Approximations (2017)
  • Rényi Divergence Variational Inference (2016)
  • Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels (2015)
  • Neural Adaptive Sequential Monte Carlo (2015)
  • Stochastic Expectation Propagation (2015)
  • Tree-structured Gaussian Process Approximations (2014)