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Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However the scalability of these models to big(More)
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have(More)
Reinforcement learning (RL) algorithms solve general sequential decision making problems through learning by trial and error. Many reinforcement learning algorithms are proven to find a good or optimal controller, but may take many interactions with the environment to do so. For real world tasks, this is often impractical, as letting a learner interact with(More)
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