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Use this template to propose a new area page. A good rule-of-thumb to decide whether a topic merits an area page is whether it would pass muster as a workshop at a machine learning venue (e.g. ICML).
Please address the following questions when raising this issue:
what story you might tell about the topic's importance to data-centric AI
whether this topic is related to other areas in data-centric AI, and why existing discussions may not be sufficient
what subtopics, resources and related work you may discuss in the area page
If your request to add the new area is approved, you can submit a PR with the following changes:
add an additional area in README.md
add an area page with relevant discussion
Dataframe preprocessing platform:
Automunge is a resource for encoding dataframes for supervised learning. Originally built as a resource for missing data infill, the library evolved to include a unique and refined API for engineering sets of feature transformations, including some novel approaches for integrating stochasticity into supervised learning training or inference. The library has been shared in workshops at venues like NeurIPS, ICML, and ICLR. We suggest the tutorials folder in the github account as a starting point, or full (and comprehensive) documentation is provided in the readme file. Further write-ups are available in the arxiv literature authored by the developer (Nicholas J. Teague) and linked from automunge.com
The text was updated successfully, but these errors were encountered:
Use this template to propose a new area page. A good rule-of-thumb to decide whether a topic merits an area page is whether it would pass muster as a workshop at a machine learning venue (e.g. ICML).
Please address the following questions when raising this issue:
If your request to add the new area is approved, you can submit a PR with the following changes:
Dataframe preprocessing platform:
Automunge is a resource for encoding dataframes for supervised learning. Originally built as a resource for missing data infill, the library evolved to include a unique and refined API for engineering sets of feature transformations, including some novel approaches for integrating stochasticity into supervised learning training or inference. The library has been shared in workshops at venues like NeurIPS, ICML, and ICLR. We suggest the tutorials folder in the github account as a starting point, or full (and comprehensive) documentation is provided in the readme file. Further write-ups are available in the arxiv literature authored by the developer (Nicholas J. Teague) and linked from automunge.com
The text was updated successfully, but these errors were encountered: