a Long-Short-Term-Memory (LSTM [@hochreiter1997long]) cell. In this design the input to the LSTM cell at each time step is an embedding of the identity of selected feature, concatenated to the actual value of the feature.

Experimental Results {#sec:experimental}
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We begin this section by a visual demonstration of the feature selection process. We then report our primary results in predicting 4-year mortality from the national survey dataset, and comparing it to major off-the-shelf baselines. We then provide results of ablation studies, justifying our main design choices, and of experimenting with our approach under off-policy regime. All results reported in this section are averaged over 5 identical trials with random splits of the dataset to train (67%) and test (33%) sets.

Demonstration on Mnist
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For the purpose of demonstration, we applied our approach on the Mnist handwritten digits dataset, where each pixel is a feature. The goal is to predict the handwritten digits based on at most five pixels. The agent was able to correctly recognize the handwritten digit from at most 5 pixels 56.9% of the time. Figure \[fig:mnist\] shows examples of predictions made by the algorithm, and the corresponding selected features.

![Demonstration of the proposed approach on the 