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Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling
We analyze alternating minimization for column space recovery of a partially observed, approximately low rank matrix with a growing number of columns and a fixed budget of observations per column.
Learning Interpretable Models with Causal Guarantees
We propose a framework for learning interpretable models from observational data that can be used to predict individual treatment effects (ITEs).
Interpretability via Model Extraction
The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions.
Interpreting Blackbox Models via Model Extraction
Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.
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