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

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

Regarding "plots are noisy and don't really support well the claim that the algorithm recovers the true Check the sharp jump in Figure 2 which is expected based on Theorem 3. Similarly, Figure 3 shows that Markov blanket can be recovered with sufficient number of observational data. NP-hard [Chickering, 1996, Learning Bayesian Networks Is NP-Complete]. Rank-2 is only used for clarity. Reviewer 2 has asked to present a case where Assumption 4 is violated. Assume that every variable can take 4 values.

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

This approach motivates interpretable yet expressive models that are capable of capturing a variety of complex dependence structures.

The inference of others' intentions is called "theory of mind"

In this paper, we propose a novel parameter estimator for probabilistic models on discrete space. The proposed estimator is derived from minimization of homogeneous divergence and can be constructed without calculation of the normalization constant, which is frequently infeasible for models in the discrete space. We investigate statistical properties of the proposed estimator such as consistency and asymptotic normality, and reveal a relationship with the information geometry. Some experiments show that the proposed estimator attains comparable performance to the maximum likelihood estimator with drastically lower computational cost.

Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult for non-experts to use.

Subsequently, it adapts its model in order to better predict future symbols.

Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowd-sourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets--named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels.