Fundamental Limits of Learning for Generalizability, Data Resilience, and Resource Efficiency
With the advancement of machine learning models and the rapid increase in their range of applications, learning algorithms should not only have the capacity to learn complex tasks, but also be resilient to imperfect data, all while being resource efficient. This thesis explores trade-offs between th...
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/155479 |