KDD 2023 - Deep Pipeline Embeddings for AutoML
Sebastian Pineda Arango, University of Freiburg
How can one effectively search for Machine Learning and Deep Learning Pipelines? Typically, pipelines contain numerous conditional hyperparameters and correlated features. Moreover, they often result in large search spaces. We propose learning an embedding function that enables a more efficient search. This function is implemented using a neural network, which can be meta-learned or designed based on knowledge of the pipeline structure. We demonstrate that this approach outperforms the state-of-the-art method. Additionally, it can be easily adapted to new components added to the pipeline.
Watch video KDD 2023 - Deep Pipeline Embeddings for AutoML online, duration hours minute second in high quality that is uploaded to the channel Association for Computing Machinery (ACM) 12 July 2023. Share the link to the video on social media so that your subscribers and friends will also watch this video. This video clip has been viewed 118 times and liked it 5 visitors.
