Preprint
Revenue-Optimal Efficient Mechanism Design with General Type Spaces
How Much Data Is Sufficient to Learn High-Performing Algorithms?
Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization
LEARNING TO RELAX: SETTING SOLVER PARAMETERS ACROSS A SEQUENCE OF LINEAR SYSTEM INSTANCES
New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs
SPECTRALLY TRANSFORMED KERNEL REGRESSION