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

These embeddings are decoded by a single four-layer convolutional network to generate notes from nearly 1000 instruments, 65 pitches per instrument on average and 5 velocities.

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

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

However, many real-world problems inherently have multiple goals, where multi-objective formulations are more natural.

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

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

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

Despite their impressive performance, Deep Neural Networks (DNNs) typically underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning tasks. W e propose that applying a different regularization coefficient to each weight might boost the performance of DNNs by allowing them t o make more use of the more relevant inputs. However, this will lead to an int ractable number of hyperparameters. Here, we introduce Regularization Learning Networks (RLNs), which overcome this challenge by introducing an efficient hy perparameter tuning scheme which minimizes a new Counterfactual Loss . Our results show that RLNs significantly improve DNNs on tabular datasets, and achieve comparable results to GBTs, with the best performance achieved with an ensemble that combines GBTs and RLNs. RLNs produce extremely sparse networks, elim inating up to 99 .

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