I let you choose some linear functionals, and then tell you the value of each one on some unknown sparse vector (compressed sensing).
We play an iterated game with unknown payoffs; you observe your payoff in each round, but nothing more, and want to maximize total payoff (multiplicative weights).
Put even more simply, what is the Bayesian method that plays randomly in rock-paper-scissors against an unknown adversary? Minimax play seems like a canonical example of a frequentist method; if you have any fixed model of your adversary you might as well play deterministically (at least if you are doing consequentialist loss minimization).
I admit I’m not familiar with either of those… Can you make a simple example of an “adversarial setting where no prior is available”?
I let you choose some linear functionals, and then tell you the value of each one on some unknown sparse vector (compressed sensing).
We play an iterated game with unknown payoffs; you observe your payoff in each round, but nothing more, and want to maximize total payoff (multiplicative weights).
Put even more simply, what is the Bayesian method that plays randomly in rock-paper-scissors against an unknown adversary? Minimax play seems like a canonical example of a frequentist method; if you have any fixed model of your adversary you might as well play deterministically (at least if you are doing consequentialist loss minimization).
The minimax estimator can be related to Bayesian estimation through the concept of a “least-favorable prior”.