Sampling-based Algorithms for Fast and Deployable AI
We present sampling-based algorithms with provable guarantees to alleviate the increasingly prohibitive costs of training and deploying modern AI systems. At the core of this thesis lies importance sampling, which we use to construct representative subsets of inputs and compress machine learning mod...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139924 https://orcid.org/0000-0002-6776-9493 |