Humanity faces numerous problems of common-pool resource appropriation.

Prevailing batch Bayesian optimisation methods allow all control variables to be freely altered at each iteration. Real-world experiments, however, often have physical limitations making it time-consuming to alter all settings for each recommendation in a batch. This gives rise to a unique problem in BO: in a recommended batch, a set of variables that are expensive to experimentally change need to be fixed, while the remaining control variables can be varied. We formulate this as a process-constrained batch Bayesian optimisation problem. We propose two algorithms, pc-BO(basic) and pc-BO(nested).

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning.

Therefore a reasonable alternative is to be able to automatically generate new annotated training samples using a process known as data augmentation.

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias. Existing ITE estimation methods mainly focus on balancing the distributions of control and treated groups, but ignore the local similarity information that provides meaningful constraints on the ITE estimation. In this paper, we propose a local similarity preserved i ndividual t reatment effect (SITE) estimation method based on deep representation learning. SITE preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch. Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods.

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

On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM.

Machine understanding of complex images is a key goal of artificial intelligence.

NNS problem and should be used for the original data.

In this paper, we revisit the Empirical Risk Minimization problem in the non-interactive local model of differential privacy.