Neural Information Processing Systems http://nips.cc/

In this paper, we consider the problem of two-sample testing of large graphs.

Neural Information Processing Systems http://nips.cc/

In this paper, we propose a new off-policy estimator that applies IS directly on the stationary state-visitation distributions to avoid the exploding variance faced by existing methods.

KGs can be represented as a multigraph, where entities such as Bill Gates and Seattle are nodes, connected with zero or more relations such as livesIn and likes .

In addition, our algorithm also adapts to scenarios where the repulsion is only required among nearby few items in the result sequence. We apply the proposed algorithm to generate relevant and diverse recommendations.

Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget.

Among those variational regularization models are one of the most popular approaches.

Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's

Experiments on multiple benchmarks show the advantage of our method compared to strong baselines.