# On the tight constant in the multivariate Dvoretzky–Kiefer–Wolfowitz inequality

@article{Naaman2021OnTT, title={On the tight constant in the multivariate Dvoretzky–Kiefer–Wolfowitz inequality}, author={Michael Naaman}, journal={Statistics \& Probability Letters}, year={2021}, volume={173}, pages={109088} }

## 13 Citations

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An empirical estimate for the expected accuracy improvement from an additional iteration of hyperparameter search is established and is bound with an error of O (cid:18)q log kk ( cid:19) w.p.h. where k is the current number of iterations.

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This paper casts the bilateral trade problem in a regret minimization framework over ) rounds of seller/buyer interactions, with no prior knowledge on their private valuations, and proves the following tight bounds on the regret.

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It is proved that R2B satisﬁes optimality and bias guarantees and empirically that it can lead to an improvement over two baselines: treating multiclass problems as multi-label by debiasing labels independently and transforming the features instead of the labels.

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A universal probabilistic spike count model is presented that defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations.

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A novel approach, MLDemon, which integrates both unlabeled data and a small amount of on-demand labels to produce a real-time estimate of the ML model’s current performance on a given data stream and is theoretical analysis to show that MLDemon is minimax rate optimal for a broad class of distribution drifts.

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