3, 2451–2487 (arXiv:1903.09857, journal link). 4. A Spectral Gap Estimate and Applications (with Bogdan Georgiev and Stefan Steinerberger), Potential

arXiv:1904.13276v1 [econ.TH] 30 Apr 2019 Tax Mechanisms and Gradient Flows Stefan Steinerberger∗ Yale University Aleh Tsyvinski Yale University† May1,2019 Abstract We demonstrate how a static optimal income taxation problem can be analyzed using dynamical methods. Speciﬁcally, we show that the taxation problem is intimately connected

5. Sean O'Rourke and Stefan Steinerberger. A nonlocal transport equation modeling complex. 8 May 2018 arXiv:1708.05373v2 [math.CA] 8 May 2018.

(with O r Lindenbaum), Re ned Least Squares for Support Recovery, arXiv:2103.10949 147. 2018-04-06 · Authors: Jianfeng Lu, Matthias Sachs, Stefan Steinerberger (or arXiv:1804.02327v1 [math.NA] for this version) Submission history There Is No Preview Available For This Item This item does not appear to have any files that can be experienced on Archive.org. Spectral methods have proven to be a highly effective tool in understanding the intrinsic geometry of a high-dimensional data set $\left\{x_i 2019-09-19 · arXiv:1909.09046 (math) [Submitted on 19 Sep 2019 ( v1 ), last revised 6 Mar 2020 (this version, v2)] Title: On the Wasserstein Distance between Classical Sequences and the Lebesgue Measure Department of Mathematics University of Washington Administrative Office C-138 Padelford Box 354350 Seattle, WA 98195-4350 Phone: (206) 543-1150 Fax: (206) 543-0397 Stefan Steinerberger's 186 research works with 855 citations and 2,563 reads, including: Randomly aggregated least squares for support recovery 2015-07-01 · Donate to arXiv. Please join the From: Stefan Steinerberger Wed, 1 Jul 2015 15:41:19 UTC (165 KB) Sun, 13 Sep 2015 21 2017-12-25 · t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years. Efficient implementations of t-SNE are available, but they scale poorly to datasets with hundreds of thousands to millions of high dimensional data-points. We present Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt 2020-12-15 · Donate to arXiv.

“Spectral echolocation via the wave embedding.” Applied and Computational Harmonic Analysis, 2017.

Lederman, Roy R; Steinerberger, Stefan. Stability Estimates for Truncated Fourier and Laplace Transforms Technical Report. (arXiv:1605.03866), 2016. Abstract

arXiv:1907.13044v1 [math.AP] 30 Jul 2019 HOT SPOTS IN CONVEX DOMAINS ARE IN THE TIPS (UP TO AN INRADIUS) STEFAN STEINERBERGER Abstract. Let Ω ⊂ R2 be a bounded, convex domain and let −∆φ1 = µ1φ1 be the ﬁrst nontrivial Laplacian eigenfunction with Neumann boundary con-ditions.

arXiv:1904.13276v1 [econ.TH] 30 Apr 2019 Tax Mechanisms and Gradient Flows Stefan Steinerberger∗ Yale University Aleh Tsyvinski Yale University† May1,2019 Abstract We demonstrate how a static optimal income taxation problem can be analyzed using dynamical methods. Speciﬁcally, we show that the taxation problem is intimately connected

OSCILLATORY FUNCTIONS VANISH ON A LARGE SET. STEFAN STEINERBERGER. Abstract.

(with Ofir Lindenbaum, Gal Mishne, and Ronen Talmon) Kernel-based parameter estimation of dynamical systems with unknown observation functions, accepted to Chaos, arxiv 2009.04142. (with Stefan Steinerberger) I completed my PhD in applied math at Yale in May 2019 under the supervision of Ronald R. Coifman and Stefan Steinerberger. You can find me on the mathematics genealogy project here. During the summer of 2018 I was a mentor for a SUMRY undergraduate research group (see our paper arXiv:1902.06633 below). 2020-09-20 · Stefan Steinerberger. University of Washington ( email) box 354350 Seattle, WA 98195-4350 United States.
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Authors: Stefan Tappe. Comments: 18 pages.

“Eigenvector localization on data-dependent graphs.” I completed my PhD in applied math at Yale in May 2019 under the supervision of Ronald R. Coifman and Stefan Steinerberger. You can find me on the mathematics genealogy project here. During the summer of 2018 I was a mentor for a SUMRY undergraduate research group (see our paper arXiv:1902.06633 below). Shahar Kovalsky.
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We study the problem of exact support recovery based on noisy observations and present Refined Least Squares (RLS). Given a set of noisy measurement $$ \\myvec{y} = \\myvec{X}\\myvecθ^* + \\myvecω,$$ and $\\myvec{X} \\in \\mathbb{R}^{N \\times D}$ which is a (known) Gaussian matrix and $\\myvecω \\in \\mathbb{R}^N$ is an (unknown) Gaussian noise vector, our goal is to recover the support of

Given a set of noisy measurement $$ \\myvec{y} = \\myvec{X}\\myvecθ^* + \\myvecω,$$ and $\\myvec{X} \\in \\mathbb{R}^{N \\times D}$ which is a (known) Gaussian matrix and $\\myvecω \\in \\mathbb{R}^N$ is an (unknown) Gaussian noise vector, our goal is to recover the support of t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years. Efficient implementations of t-SNE are available, but they scale poorly to datasets with hundreds of thousands to millions of high dimensional data-points. We present Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens: Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations. CoRR abs/1902.05804 ( 2019 ) Department of Mathematics University of Washington Administrative Office C-138 Padelford Box 354350 Seattle, WA 98195-4350 Phone: (206) 543-1150 Fax: (206) 543-0397 arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.