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Bayesian Adaptive Data Analysis Guarantees from Subgaussianity
The new field of adaptive data analysis seeks to provide algorithms and provable guarantees for models of machine learning that allow researchers to reuse their data, which normally falls outside of the usual statistical paradigm of static data analysis.
Challenges in Bayesian Adaptive Data Analysis
Traditional statistical analysis requires that the analysis process and data are independent.
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
We show how to approximate a data matrix $\mathbf{A}$ with a much smaller sketch $\mathbf{\tilde A}$ that can be used to solve a general class of constrained k-rank approximation problems to within $(1+\epsilon)$ error.
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