# Learning by stochastic serializations

## TL;DR

This paper introduces a generic learning framework that maps complex structures to serializations, enabling the use of sequence-based density estimators, with sampling methods that preserve structural properties and improve learning efficiency.

## Contribution

It proposes a novel serialization approach for complex structures, allowing generic sequence-based learning applicable across various structures, with effective sampling to capture structural statistics.

## Key findings

- Competitive or superior to specialized algorithms
- Provides protection from overfitting through sampling
- Effective sampling preserves structural statistics

## Abstract

Complex structures are typical in machine learning. Tailoring learning algorithms for every structure requires an effort that may be saved by defining a generic learning procedure adaptive to any complex structure. In this paper, we propose to map any complex structure onto a generic form, called serialization, over which we can apply any sequence-based density estimator. We then show how to transfer the learned density back onto the space of original structures. To expose the learning procedure to the structural particularities of the original structures, we take care that the serializations reflect accurately the structures' properties. Enumerating all serializations is infeasible. We propose an effective way to sample representative serializations from the complete set of serializations which preserves the statistics of the complete set. Our method is competitive or better than state of the art learning algorithms that have been specifically designed for given structures. In addition, since the serialization involves sampling from a combinatorial process it provides considerable protection from overfitting, which we clearly demonstrate on a number of experiments.