Our paper titled “How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design” (link on the publication page), co-authored with Ellen Vitercik, Travis Dick, Toumas Sandholm, Nina Balcan, and Carl Kingsford was accepted to STOC 2021. The acceptance rate for this year’s (virtual) STOC meeting was only 28%.