Lecture 21: Minimax Theory

Lecture 12 : Minimax Decision Theory and Asymptotics

CS 364 A : Algorithmic Game Theory Lecture # 18 : From External Regret to Swap Regret and the Minimax Theorem ∗

Last lecture we proved that coarse correlated equilibria (CCE) are tractable, in a satisfying sense: there are simple and computationally efficient learning procedures that converge quickly to the set of CCE. Of course, if anything in our equilibrium hierarchy (Figure 1) was going to be tractable, it was going to be CCE, the biggest set. The good researcher is never satisfied and always seeks s...

Lecture 16 : Duality and the Minimax theorem

Last lecture we introduce linear programs (LP) and saw how to model problems with LPs. In this lecture we study duality of LPs. Roughly speaking, the dual of a minimization LP is a maximization LP which its solutions provide lower bounds for the optimum of the original LP. First we show how to construct dual of an LP. Then we talk about relation between the optimum of an LP and its dual. In par...

Lecture 20 : April 2 , Minimax Theory & Testing

STATS 300 A : Theory of Statistics Fall 2015 Lecture 11 — October 27

This lemma allows us to find a minimax estimator for a particular tractable submodel, and then show that the worst-case risk for the full model is equal to that of the submodel (that is, the worst-case risk doesn’t rise as you go to the full model). In this case, using the Lemma, we can argue that the estimator we found is also minimax for the full model. This was similar to how we justified mi...