Equal Contribution 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. is often attributed to the rapid decay in the learning rate when gradients are dense, which is often the case in many machine learning applications.

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The proper identification of anomalies can be critical for many domains.

In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution.

Each of these concepts have been individually explored in previous studies.

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Implicit feedback is widely used in collaborative filtering methods for recommendation.

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.

In fact, the additive noise model assumes that all categories of the variables are placed in the "right" order; furthermore, if