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Found 3 papers, 2 papers with code
Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans
In contrast to existing work, our key idea is to extract "logical query plans" from ML pipeline code relying on popular libraries.
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to their findings.
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
There are exponentially many terms in the multilinear extension, and one key contribution of this paper is a polynomial time algorithm that computes exactly, given a retrieval-augmented model with an additive utility function and a validation set, the data importance of data points in the retrieval corpus using the multilinear extension of the model's utility function.
