Talk Explores Machine Learning for Discrete Optimization With Theoretical Guarantees

Can machine learning improve discrete optimization algorithms without sacrificing theoretical guarantees? This was the central question of the talk I gave this spring at a few schools (UCSD, UIC, Yale, Penn): Machine Learning for Discrete Optimization: Theoretical Foundations.