Abstract:The equivalence of realizable and agnostic learnability is a fundamental
phenomenon in learning theory. With variants ranging from classical settings
like PAC learning and regression to recent trends such as adversarially robust
learning, it's surprising that we still lack a unified theory; traditional
proofs of the equivalence tend to be disparate, and rely on strong
model-specific assumptions like uniform convergence and sample compression.
In this work, we give the first model-independent framework explai… Show more
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