Statistical Learning with Discrete Structures: Statistical and Computational Perspectives
In various statistical tasks it is of interest to learn estimators with discrete structures (e.g., sparsity, low-rank, shared model parameters, etc)---they are appealing for their interpretability and compactness. However, learning with discrete structures can be computationally challenging. In thi...
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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/1721.1/155499 |