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Agnostic smoothed online learning is now achievable without knowing the base measure or smoothing parameter, matching statistical learning's flexibility with online learning's robustness.
Access to simulators can yield classical learning guarantees even in the presence of complex data dependencies, fundamentally shifting our understanding of learning theory.
