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Reinforcement learning utilizing kernel ridge regression to predict the expected value function represents a powerful method with great representational capacity. This setting is a highly versatile framework amenable to analytical results. Weconsider kernel-based function approximation for RL in the infinite horizon average reward setting, also referred toasthe undiscounted setting. Wepropose an optimistic algorithm, similar to acquisition function based algorithms in the special caseofbandits.

We consider the problem of automatic posterior inference. A scientist or expert creates a model p(z,x) for latent variablesz and observed variablesx.

Unsupervised video domain adaptation is a practical yet challenging task.