8 citations · 25 across the 10 of their papers we have counts for
13 papers
Contextual Dynamic Pricing with Heterogeneous Buyers
Thodoris Lykouris, Sloan Nietert, Princewill Okoroafor +2
We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over rounds) that depend on the observabl…
Estimation of Stochastic Optimal Transport Maps
Sloan Nietert, Ziv Goldfeld
The optimal transport (OT) map is a geometry-driven transformation between high-dimensional probability distributions which underpins a wide range of tasks in statistics, applied p…
Robust Alignment via Partial Gromov-Wasserstein Distances
Xiaoyun Gong, Sloan Nietert, Ziv Goldfeld
The Gromov-Wasserstein (GW) problem provides a powerful framework for aligning heterogeneous datasets by matching their internal structures in a way that minimizes distortion. Howe…
Robust Distribution Learning with Local and Global Adversarial Corruptions
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
We consider learning in an adversarial environment, where an -fraction of samples from a distribution are arbitrarily modified (global corruptions) and the remaini…
Robust Estimation under the Wasserstein Distance
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
We study the problem of robust distribution estimation under the Wasserstein distance, a popular discrepancy measure between probability distributions rooted in optimal transport (…
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…
Learning in Stackelberg Games with Non-myopic Agents
Nika Haghtalab, Thodoris Lykouris, Sloan Nietert +1
We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent, without knowing the agent's payoff function. Although learning in Stackelberg…
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…
Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis
Sloan Nietert, Rachel Cummings, Ziv Goldfeld
The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and ma…
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…
Smooth -Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Sloan Nietert, Ziv Goldfeld, Kengo Kato
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the cur…
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…
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…