Automated Interpretation of Machine Learning Models
As machine learning (ML) models are increasingly deployed in production, there’s a pressing need to ensure their reliability through auditing, debugging, and testing. Interpretability, the subfield that studies how ML models make decisions, aspires to meet this need but traditionally relies on human...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156277 |