[KDD 2023] Deep Pipeline Embeddings for AutoML
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Updated
Jul 1, 2025 - Python
[KDD 2023] Deep Pipeline Embeddings for AutoML
This project is for understanding and quantifying the errors in a machine learning or data analytic pipeline. Two approaches are explored. The first is using freezing and unfreezing of pipeline components (using optimization techniques like grid-search, random-search, Bayesian Optimization, Genetic Algorithms etc.). The second is using a gradien…
AutostreamPipe: LLM Assisted Automatic Stream Processing Pipeline Generation
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