An important problem that arises in reinforcement learning and Monte Carlo methods is
estimating quantities defined by the stationary distribution of a Markov chain. In many real …

The instability in GAN training has been a long-standing problem despite remarkable
research efforts. We identify that instability issues stem from difficulties of performing feature …

We propose a novel method for solving partial differential equations using multi-fidelity
physics-informed generative adversarial networks. Our approach incorporates physics …

We consider the problem of approximating the stationary distribution of an ergodic Markov
chain given a set of sampled transitions. Classical simulation-based approaches assume …

We investigate the use of parameterized families of information-theoretic measures to
generalize the loss functions of generative adversarial networks (GANs) with the objective of …

We present a new type of acquisition function for online decision-making in multi-armed and
contextual bandit problems with extreme payoffs. Specifically, we model the payoff function …

The generative adversarial network (GAN) has received considerable attention recently as a
model for data synthesis, without an explicit specification of a likelihood function. There has …

Modelling highly multi-modal data is a challenging problem in machine learning. Most
algorithms are based on maximizing the likelihood, which corresponds to the M (oment) …

Maximum likelihood (ML) and adversarial learning are two popular approaches for training
generative models, and from many perspectives these techniques are complementary. ML …

The culinary business has good prospects and is one of the growing business opportunities
today, ranging from traditional food traders with the term street vendors, buffets to modern …