activity
20192022
most citedLimit distribution theory for smooth -Wasserstein distances

4 citations · 9 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML20224 cited

Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances

Sloan Nietert, Ritwik Sadhu, Ziv Goldfeld +1

Sliced Wasserstein distances preserve properties of classic Wasserstein distances while being more scalable for computation and estimation in high dimensions. The goal of this work…

math.PR20224 cited

Limit distribution theory for smooth -Wasserstein distances

Ziv Goldfeld, Kengo Kato, Sloan Nietert +1

The Wasserstein distance is a metric on a space of probability measures that has seen a surge of applications in statistics, machine learning, and applied mathematics. However, sta…

cs.LG20211 cited

Learning with Comparison Feedback: Online Estimation of Sample Statistics

Michela Meister, Sloan Nietert

We study an online version of the noisy binary search problem where feedback is generated by a non-stochastic adversary rather than perturbed by random noise. We reframe this as ma…

math-ph2019

Rigidity and a common framework for mutually unbiased bases and k-nets

Sloan Nietert, Zsombor Szilágyi, Mihály Weiner

Many deep, mysterious connections have been observed between collections of mutually unbiased bases (MUBs) and combinatorial designs called -nets (and in particular, between com…

math.MG2019

Polarization, sign sequences and isotropic vector systems

Gergely Ambrus, Sloan Nietert

We determine the order of magnitude of the th -polarization constant of the unit sphere for every and . For , we prove that extremizers…