However, the undirected setting is often insufficient: only a causal (i.e., directed) network provides

Remaining a gradient-based method, it is also the first Bayesian model-agnostic meta-learning method applicable to various tasks including reinforcement learning.

Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning.

In this paper, we focus on the problem of community detection for a simple two-class Stochastic Blockmodel (SBM).

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

A contextual bandit is an online learning framework for modeling sequential decision-making problems.

Potential-based reward shaping incorporates prior domain knowledge in the form of additional rewards provided during training to speed up convergence of reinforcement learning algorithms, without changing the optimal policies (Ng et al. [1999]).

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