Large-Scale Learning and Inference: What We Have Learned with Markov Logic Networks
On the Quantitative Analysis of Deep Belief Networks
Building Machines that Imagine and Reason: Principles and Applications of Deep Generative Models
Data-Dependent Geometries and Structures: Analyses and Algorithms for Machine Learning
Convex Analysis and Optimization
Bayesian Methods
Activation and control of extreme trajectories in network dynamics
Electromechanical Dynamics
Making centralized (graph) computation faster, distributed and (at times) better
Principles of Digital Communication II