Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time.

We are only aware of few approaches devoted to infer the Group Lasso structure.

We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks to the well-developed theory of information geometry, the reconstructed tensor is unique and always minimizes the KL divergence from an input tensor. We empirically show that Legendre decomposition can more accurately reconstruct tensors than other nonnegative tensor decomposition methods.

We aim at using a large observational sample and a possibly much smaller experimental sample.

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

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

Novelty detection is the problem of identifying whether a new data point is considered to be an inlier or an outlier. We assume that training data is available to describe only the inlier distribution. Recent approaches primarily leverage deep encoder-decoder network architectures to compute a reconstruction error that is used to either compute a novelty score or to train a one-class classifier.

Link prediction is a key problem for network-structured data.

In this paper we build on the Optimal Rewards Framework of Singh et al. [2010] that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the

Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series.