Robustly Reliable Learning for Regression
The work introduces a method for robustly reliable learning in regression, aiming to find an optimal prediction range for a test point despite corrupted training data. It achieves this by determining the tightest possible range consistent with all but at most $b$ points. The solution is fixed-parameter tractable, meaning its complexity depends on the corruption budget $b$, making it efficient for finding reliable predictions.