Remi Rampin

Iddo Drori, Yamuna Krishnamurthy, Remi Rampin et al.

We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play. AlphaD3M is based on edit operations performed over machine learning pipeline primitives providing explainability. We compare AlphaD3M with state-of-the-art AutoML systems: Autosklearn, Autostacker, and TPOT, on OpenML datasets. AlphaD3M achieves competitive performance while being an order of magnitude faster, reducing computation time from hours to minutes, and is explainable by design.

Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenco et al.

Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached state-of-the-art results with an order of magnitude speedup using reinforcement learning with self-play. In this work we extend AlphaD3M by using a pipeline grammar and a pre-trained model which generalizes from many different datasets and similar tasks. Our results demonstrate improved performance compared with our earlier work and existing methods on AutoML benchmark datasets for classification and regression tasks. In the spirit of reproducible research we make our data, models, and code publicly available.

Remi Rampin, Fernando Chirigati, Vicky Steeves et al.

Reproducibility in the computational sciences has been stymied because of the complex and rapidly changing computational environments in which modern research takes place. While many will espouse reproducibility as a value, the challenge of making it happen (both for themselves and testing the reproducibility of others' work) often outweigh the benefits. There have been a few reproducibility solutions designed and implemented by the community. In particular, the authors are contributors to ReproZip, a tool to enable computational reproducibility by tracing and bundling together research in the environment in which it takes place (e.g. one's computer or server). In this white paper, we introduce a tool for unpacking ReproZip bundles in the cloud, ReproServer. ReproServer takes an uploaded ReproZip bundle (.rpz file) or a link to a ReproZip bundle, and users can then unpack them in the cloud via their browser, allowing them to reproduce colleagues' work without having to install anything locally. This will help lower the barrier to reproducing others' work, which will aid reviewers in verifying the claims made in papers and reusing previously published research.