Search Results for author:
Found 1 papers, 0 papers with code
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
We provide a broadly applicable theory for deriving generalization guarantees that bound the difference between the algorithm's average performance over the training set and its expected performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
