Neural Information Processing Systems http://nips.cc/

This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order.

We consider two versions: sequential, where Bob observes Alice's cut

Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances.

We study the problem of how to repeatedly sell to a buyer running a no-regret,mean-based algorithm. Previous work [Braverman et al., 2018] shows that it ispossible to design effective mechanisms in such a setting that extract almost allof the economic surplus, but these mechanisms require the buyer's values each

Neural Information Processing Systems http://nips.cc/

Several papers consider approaches that limit the number of bits to represent floating point numbers [13, 24, 31]. Recent work proposes adaptive tuning of the compression ratio [7].

The number of features is comparable to the sample size and errors may be heavy-tailed.