We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These
models combine two trained components: a base distribution (generally an implicit model) …

We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and
study its convergence properties. The MMD is an integral probability metric defined for a …

Abstract Machine learning has achieved tremendous success in a variety of domains in
recent years. However, a lot of these success stories have been in places where the training …

Neural networks provide a rich class of high-dimensional, non-convex optimization
problems. Despite their non-convexity, gradient-descent methods often successfully …

Electricity prices in spot markets are volatile and can be affected by various factors, such as
generation and demand, system contingencies, local weather patterns, bidding strategies of …

We propose a principled method for gradient-based regularization of the critic of GAN-like
models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy …

Generative adversarial networks, or GANs, commonly display unstable behavior during
training. In this work, we develop a principled theoretical framework for understanding the …

We study minimax convergence rates of nonparametric density estimation under a large
class of loss functions called``adversarial losses'', which, besides classical L^ p losses …

We study the problem of estimating a nonparametric probability distribution under a family of
losses called Besov IPMs. This family is quite large, including, for example, L^ p distances …

While likelihood-based inference and its variants provide a statistically efficient and widely
applicable approach to parametric inference, their application to models involving …