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.
citing papers explorer
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On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy
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.
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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.
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Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval
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.