# WAX-ML: A Python library for machine learning and feedback loops on streaming data

arXiv: https://arxiv.org/abs/2106.06524 · HTML: https://mlsanity.com/paper/2106.06524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [FNet: Mixing Tokens with Fourier Transforms](https://mlsanity.com/paper/2105.03824/) (2021)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [The Nonstochastic Control Problem](https://mlsanity.com/paper/1911.12178/) (2019)
- [Online Learning for Time Series Prediction](https://mlsanity.com/paper/1302.6927/) (2013)
- [EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy](https://mlsanity.com/paper/2008.04175/) (2020)

## Abstract (arXiv)

Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and feedback loops working on streaming data. It strives to complement JAX with tools dedicated to time series. WAX-ML makes JAX-based programs easy to use for end-users working with pandas and xarray for data manipulation. It provides a simple mechanism for implementing feedback loops, allows the implementation of online learning and reinforcement learning algorithms with functions, and makes them easy to integrate by end-users working with the object-oriented reinforcement learning framework from the Gym library. It is released with an Apache open-source license on GitHub at https://github.com/eserie/wax-ml.