The importance-weighted extension is appropriate for estimating complete counterfactual distributions of rewards given data from a randomized experiment, e.g.,

In this work, we explore restricting the degrees of freedom of convolutional kernels by imposing a structure on them. This structure can be thought of as constructing the convolutional kernel by super-imposingseveral constant-heightkernels.

What makes a classifier have the ability to generalize? There have been a lot of important attempts to address this question, but a clear answer is still elusive.

Tothatend, let : Rd ! The "easy" direction1: if and satisfy (X)= U (X)almostsurelyd ( , )= d (U , )= 0. Wesk anddefertheA.2.

Ourmajorcontributionsinclude: We identify common pitfalls in conventional MoE such as load imbalance as described inSection 3.1.

Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use, by constructing a model of the environment's dynamics that correctly predicts the observed state transitions. In this paper we argue that the limited representational resources of model-based RL agents are better used to build models that are directly useful for value-based planning.