Derives McDiarmid-type inequalities for dependent variables via approximate tensorization of entropy, with applications improving DKW rates to 1/sqrt(n) under weak dependence for log-concave measures.
arXiv preprint arXiv:1511.05240 , year=
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RAIC unifies uniform recovery of structured signals from nonlinear observations via PGD, yielding error rates comparable to nonuniform guarantees up to log factors in sparse and 1-bit settings.
EMMETT and IRENE enable on-the-fly synthesis of classifiers for novel items in extreme classification, yielding up to 15% Recall@10 gains in zero-shot retrieval and 4.2% CTR lift in a production A/B test.
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Robust Uniform Recovery of Structured Signals from Nonlinear Observations
RAIC unifies uniform recovery of structured signals from nonlinear observations via PGD, yielding error rates comparable to nonuniform guarantees up to log factors in sparse and 1-bit settings.