Let’s Stop Leaving Money on the Table | Richard M. Karp Distinguished Lecture

Katrina Ligett explores why learning theory can make pessimistic predictions even when practical systems succeed. Instead of analysing only worst-case algorithms and data, she proposes reformulating questions around properties that explain algorithmic success and instance-specific performance. Examples in generalisation and privacy illustrate how these perspectives could narrow the gap between theory and empirical practice and create new mathematical questions.