Publications

A number of decarbonization scenarios for the energy sector are built on simultaneous electrification of energy demand, and decarbonization of electricity generation through renewable energy sources. However, increased electricity demand due to heat and transport electrification and the variability associated with renewables have the potential to disrupt stable electric grid operation. To address these issues using demand response, researchers ...

We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine formulation for kernel PCA by orthogonality constraints on the latent variables to promote disentanglement and to make it possible to carry out optimization without first defining a stabilized objective. After discussing a number of algorithms for end-to-end training, ...

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the model has a primal and dual formulation to incorporate both kernel-based and (deep convolutional) neural network based models within ...

Kernel methods have achieved very good performance on large scale regression and classification problems by using the Nyström method and preconditioning techniques. The Nyström approximation---based on a subset of landmarks---gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization. We further elaborate on the impact of sampling diverse landmarks for constructing the Nyström approximation in ...