No Free Lunch: The Out-of-Distribution Dilemma

In traditional machine learning, we expected training and evaluation distribution to match. If model didn’t perform on data outside that distribution, we raised our hands, cited No Free Lunch theorem and called it quits. People scoffed at test sets that were “out of… continue reading