Blackwell Approachability and Low-Regret Learning are Equivalent
CoRR(2010)
摘要
We consider the celebrated Blackwell Approachability Theorem for two-player
games with vector payoffs. We show that Blackwell's result is equivalent, via
efficient reductions, to the existence of "no-regret" algorithms for Online
Linear Optimization. Indeed, we show that any algorithm for one such problem
can be efficiently converted into an algorithm for the other. We provide a
useful application of this reduction: the first efficient algorithm for
calibrated forecasting.
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