Talks

In over the last two decades we have developed good understanding how to quantify the impact of strategic user behavior on overall performance in many games (including traffic routing as well as online auctions). Early work focused on evaluating the quality of Nash equilibria. However, it turns out that the resulting bounds extend to learning out comes in repeated games under pretty general assumptions. In this talk we will review these results, which mostly assume that learners satisfy versions of the no-regret property after a long enough play. We will also discuss pros and cons of no-regret as a behavioral assumption on learning outcomes, as well as limitations of assuming no-regret as the main condition players achieve as they are learning.

In over the last two decades we have developed good understanding how to quantify the impact of strategic user behavior on overall performance in many games (including traffic routing as well as online auctions). Early work focused on evaluating the quality of Nash equilibria. However, it turns out that the resulting bounds extend to learning out comes in repeated games under pretty general assumptions. In this talk we will review these results, which mostly assume that learners satisfy versions of the no-regret property after a long enough play. We will also discuss pros and cons of no-regret as a behavioral assumption on learning outcomes, as well as limitations of assuming no-regret as the main condition players achieve as they are learning.

We will cover the fundamentals of designing experiments (i.e., picking interventions) for the purpose of learning a structural causal model. We will begin by reviewing what graphical information can be learned from interventions. Then, we will discuss basic aspects of different settings for experimental design, including the distinction between passive and active settings, possible constraints on the interventions, and the difference between noisy and noiseless settings. After establishing basic nomenclature, we will spend the bulk of our time on a survey of strategies for passive and active experimental design in the noiseless setting, emphasizing general techniques for obtaining theoretical guarantees. We will conclude with a discussion of “targeted” experimental design, in which case the learning objective may be more specific than completely learning a structural causal model, and review the potential complexity benefits.

We will cover the fundamentals of designing experiments (i.e., picking interventions) for the purpose of learning a structural causal model. We will begin by reviewing what graphical information can be learned from interventions. Then, we will discuss basic aspects of different settings for experimental design, including the distinction between passive and active settings, possible constraints on the interventions, and the difference between noisy and noiseless settings. After establishing basic nomenclature, we will spend the bulk of our time on a survey of strategies for passive and active experimental design in the noiseless setting, emphasizing general techniques for obtaining theoretical guarantees. We will conclude with a discussion of “targeted” experimental design, in which case the learning objective may be more specific than completely learning a structural causal model, and review the potential complexity benefits.

We will cover the fundamentals of designing experiments (i.e., picking interventions) for the purpose of learning a structural causal model. We will begin by reviewing what graphical information can be learned from interventions. Then, we will discuss basic aspects of different settings for experimental design, including the distinction between passive and active settings, possible constraints on the interventions, and the difference between noisy and noiseless settings. After establishing basic nomenclature, we will spend the bulk of our time on a survey of strategies for passive and active experimental design in the noiseless setting, emphasizing general techniques for obtaining theoretical guarantees. We will conclude with a discussion of “targeted” experimental design, in which case the learning objective may be more specific than completely learning a structural causal model, and review the potential complexity benefits.

We will cover the fundamentals of designing experiments (i.e., picking interventions) for the purpose of learning a structural causal model. We will begin by reviewing what graphical information can be learned from interventions. Then, we will discuss basic aspects of different settings for experimental design, including the distinction between passive and active settings, possible constraints on the interventions, and the difference between noisy and noiseless settings. After establishing basic nomenclature, we will spend the bulk of our time on a survey of strategies for passive and active experimental design in the noiseless setting, emphasizing general techniques for obtaining theoretical guarantees. We will conclude with a discussion of “targeted” experimental design, in which case the learning objective may be more specific than completely learning a structural causal model, and review the potential complexity benefits.