Benchmark prediction methods mostly work by interpolation among similar models and fail on better, unfamiliar models, where random sampling with an AIPW-style correction is the only consistent improvement.
Four lectures on probabilistic methods for data science
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Methods of high-dimensional probability play a central role in applications for statistics, signal processing theoretical computer science and related fields. These lectures present a sample of particularly useful tools of high-dimensional probability, focusing on the classical and matrix Bernstein's inequality and the uniform matrix deviation inequality. We illustrate these tools with applications for dimension reduction, network analysis, covariance estimation, matrix completion and sparse signal recovery. The lectures are geared towards beginning graduate students who have taken a rigorous course in probability but may not have any experience in data science applications.
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How Benchmark Prediction from Fewer Data Misses the Mark
Benchmark prediction methods mostly work by interpolation among similar models and fail on better, unfamiliar models, where random sampling with an AIPW-style correction is the only consistent improvement.