68 citations · 135 across the 16 of their papers we have counts for
3 papers · 1 filter
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
Maria-Florina Balcan, Dan DeBlasio, Travis Dick +3
Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit mea…
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Algorithms typically come with tunable parameters that have a considerable impact on the computational resources they consume. Too often, practitioners must hand-tune the parameter…
Estimating Approximate Incentive Compatibility
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
In practice, most mechanisms for selling, buying, matching, voting, and so on are not incentive compatible. We present techniques for estimating how far a mechanism is from incenti…