robustness

Most real datasets have wrong labels, and they cap your accuracy invisibly. Cleaning all of it by hand doesn't scale. Here is how to find the bad labels and train robustly around them.

A classic ML-security question that catches people who only know clean-data accuracy. The signal is understanding why models are brittle, the realistic threat model, and that no single defense holds. Here is the honest answer.