mlsanityData to 7 Oct 2026

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

arXiv 2106.06524 ↗First posted 11 Jun 2021cs.LG cs.CLcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 46 references that are in the map. First posted on arXiv 11 Jun 2021.

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References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019Introduction to Online Convex Optimizationcited by 793
  3. 2021Machine learning accelerated computational fluid dynamicscited by 461
  4. 2019Differentiable Convex Optimization Layerscited by 458
  5. 2021FNet: Mixing Tokens with Fourier Transformscited by 343
  6. 2020River: machine learning for streaming data in Pythoncited by 96
  7. 2019The Nonstochastic Control Problemcited by 52
  8. 2013Online Learning for Time Series Predictioncited by 33
  9. 2020EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPycited by 3

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