(Deep) Kernel Mean Embeddings for Representing and Learning on Distributions | Danica Sutherland | LIKE23

Gaussian Process Ensembles and the Bayesian Committee Machine | Nicolas Durrande | LIKE23

Geometric Gaussian Processes | Viacheslav Borovitskiy & Alexander Terenin | LIKE23

Optimal maximum mean discrepancy quantization with clustering penalties on the sparse simplex | Sebastien Da Veiga | LIKE23

Emulating and aggregating GPs with PriorVAE and πVAE: deep generative models to enable scalable MCMC inference and define a novel stochastic process | Seth Flaxman & Elizaveta Semenova | LIKE23

Scalable Gaussian Processes when you don't just care about Regression | Henry Moss | LIKE23

Sensitivity analysis on (excursion) sets based on kernel embedding of random sets | Noé Fellmann | LIKE23

Gaussian Process Prediction for a Cyclone Application using adapted Kernels | Chifaa Al Dahik | LIKE23

Two Tales of Orthogonality | James Hensman | LIKE23

Kernel Mean Embeddings meet Functional Data Analysis | George Wynne | LIKE23

Kernels for high-dimensional Gaussian Process Modeling | Mickaël Binois | LIKE23