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20132026
most citedThe Computational Limits of Deep Learning

316 citations · 661 across the 65 of their papers we have counts for

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5 papers · 1 filter

math.ST2023

Finite sample rates of convergence for the Bigraphical and Tensor graphical Lasso estimators

Shuheng Zhou, Kristjan Greenewald

Many modern datasets exhibit dependencies among observations as well as variables. A decade ago, Kalaitzis et. al. (2013) proposed the Bigraphical Lasso, an estimator for precision…

math.ST2020

-Variance: A Clustered Notion of Variance

Justin Solomon, Kristjan Greenewald, Haikady N. Nagaraja

We introduce -variance, a generalization of variance built on the machinery of random bipartite matchings. -variance measures the expected cost of matching two sets of sa…

math.ST2020★ 5 cited

Gaussian-Smooth Optimal Transport: Metric Structure and Statistical Efficiency

Ziv Goldfeld, Kristjan Greenewald

Optimal transport (OT), and in particular the Wasserstein distance, has seen a surge of interest and applications in machine learning. However, empirical approximation under Wasser…

math.ST2019

Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation

Ziv Goldfeld, Kristjan Greenewald, Yury Polyanskiy +1

This paper studies convergence of empirical measures smoothed by a Gaussian kernel. Specifically, consider approximating , for $\mathcal{N}_σ\triangleq\mathcal{…

math.ST2018★ 7 cited

Estimating Differential Entropy under Gaussian Convolutions

Ziv Goldfeld, Kristjan Greenewald, Yury Polyanskiy

This paper studies the problem of estimating the differential entropy , where and are independent -dimensional random variables with $Z\sim\mathcal{N}(0,σ^2 \mat…