Interpreting Covariance Functions & Classification
Bounding the Gaussian Process Information Gain: Applications to PAC-Bayes and GP Bandit Optimization
Kernels and Gaussian Processes
Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models
Theoretical Neuroscience and Deep Learning Theory
Weighted Deduction as an Abstraction Level for AI
Theoretical neuroscience and deep learning theory
Convex Analysis and Optimization
Bayesian Data Fusion with Gaussian Process Priors : An Application to Protein Fold Recognition
Introduction to Communication, Control, and Signal Processing