The strategic interaction of multiple parties with different objectives is at the heart of modern large scale computer systems and electronic markets. Participants face such complex decisions in these settings that the classic economic equilibrium is not a good predictor of their behavior. The analysis and design of these systems has to go beyond equilibrium assumptions. Evidence from online auction marketplaces suggests that participants rather use algorithmic learning. In the first part of the talk, I will describe a theoretical…

Most applications of machine learning across science and industry rely on the holdout method for model selection and validation. Unfortunately, the holdout method often fails in the now common scenario where the analyst works interactively with the data, iteratively choosing which methods to use by probing the same holdout data many times. In this talk, we apply the principle of algorithmic stability to design reusable holdout methods, which can be used many times without losing the guarantees of fresh data.…