Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Nietert, Sloan"'
We consider learning in an adversarial environment, where an $\varepsilon$-fraction of samples from a distribution $P$ are arbitrarily modified (global corruptions) and the remaining perturbations have average magnitude bounded by $\rho$ (local corru
Externí odkaz:
http://arxiv.org/abs/2406.06509
Distributionally robust optimization (DRO) is an effective approach for data-driven decision-making in the presence of uncertainty. Geometric uncertainty due to sampling or localized perturbations of data points is captured by Wasserstein DRO (WDRO),
Externí odkaz:
http://arxiv.org/abs/2311.05573
We study the problem of robust distribution estimation under the Wasserstein distance, a popular discrepancy measure between probability distributions rooted in optimal transport (OT) theory. Given $n$ samples from an unknown distribution $\mu$, of w
Externí odkaz:
http://arxiv.org/abs/2302.01237
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 is to quantify this scalability from three key aspects: (i) empirical
Externí odkaz:
http://arxiv.org/abs/2210.09160
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 games is well-understood when the agent is myopic, dealing with non-my
Externí odkaz:
http://arxiv.org/abs/2208.09407
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, statistical aspects of Wasserstein distances are bottlenecked by the curs
Externí odkaz:
http://arxiv.org/abs/2203.00159
The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and machine learning. Despite their rich structure and demonstrated utility,
Externí odkaz:
http://arxiv.org/abs/2111.01361
Autor:
Meister, Michela, Nietert, Sloan
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 maintaining an accurate estimate for the median of an adversarial sequen
Externí odkaz:
http://arxiv.org/abs/2101.04176
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the curse of dimensionality when estimating these distances from data, recent
Externí odkaz:
http://arxiv.org/abs/2101.04039
Many deep, mysterious connections have been observed between collections of mutually unbiased bases (MUBs) and combinatorial designs called $k$-nets (and in particular, between complete collections of MUBs and finite affine - or equivalently: finite
Externí odkaz:
http://arxiv.org/abs/1907.02469