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We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
In this paper we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. By following...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...