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 )

The article Optimal Gabor frame bounds for separable lattices and estimates for Jacobi theta functions (joint work with Stefan Steinerberger) is available online in the "Journal of Mathematical Analysis and Applications". *** [04.08.2016] ArXiv Preprint

A Spectral Gap Estimate and Applications (with Bogdan Georgiev and Stefan Steinerberger), Potential Cite as: arXiv:1301.3371 [math.AP]. (or arXiv:1301.3371v4 [math.AP] for this version). Submission history. From: Stefan Steinerberger [view email] [v1] Tue, 15 21 records ArXiv e-prints 2015 http://arxiv.org/abs/1504.03644. Alberto Enciso, Daniel Peralta-Salas and Stefan Steinerberger Prescribing the nodal set of the JIANFENG LU, CHRISTOPHER D. SOGGE, AND STEFAN STEINERBERGER. Abstract. We consider Laplacian eigenfunctions on a d−dimensional bounded.

arxiv.org:1308.4422. 6. Brasco , L., De Philippis, Correspondence to Stefan Steinerberger. New to arxiv-sanity? Check out the Showing most recent Arxiv papers: A precise local limit theorem Jeremy G. Hoskins, Stefan Steinerberger 4/8/2021( v1: Stefan Steinerberger. Abstract.

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).

One cannot STEFAN STEINERBERGER. 1.2. Geometric Nov 25, 2020 Stefan Steinerberger · Stefan Steinerberger.

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 )

“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.

Speciﬁcally, we show that the taxation problem is intimately connected Donate to arXiv. Please join the From: Stefan Steinerberger Wed, 1 Jul 2015 15:41:19 UTC (165 KB) Sun, 13 Sep 2015 21 Stefan Steinerberger (with Yulan Zhang), t-SNE, Forceful Colorings and Mean Field Limits, arxiv Max-Cut via Kuramoto-type Oscillators, arxiv A Pointwise Inequality for Derivatives of Solutions of the Heat Equation in Bounded Domains, arxiv Stefan Steinerberger Contact Information mains, arXiv:2103.17187 148. (with O r Lindenbaum), Re ned Least Squares for Support Recovery, arXiv:2103.10949 147 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 ‪University of Washington, Seattle‬ - ‪‪Cited by 1,211‬‬ - ‪Analysis‬ - ‪Partial Differential Equations‬ - ‪Spectral Theory‬ - ‪Potential Theory‬ - ‪Applied Mathematics‬ 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 ) 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 arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXiv:2004.02437. doi:10.1016/j.hm.2020.04.003. S2CID Steinerberger, Stefan (2015), New limits for traveling salesman's Advances in the probability of being On a conjecture of Faulhuber and Steinerberger on the logarithmic derivative of θ4. Ernvall-Hytönen, A-M. Palojärvi, N., 2018, arXiv. Forskningsoutput: On a conjecture of Faulhuber and Steinerberger on the logarithmic derivative of θ4 Palojärvi, N., 2018, arXiv.

Stefan Steinerberger studies Modulation, Communication Theories, and System Engineering. A joint seminar series covering a wide variety of topics in applied mathematics, PDEs, and scientific computation.
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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

CoRR abs/1902.05804 ( 2019 ) We are interested in the following problem: given an open, bounded domain $\Omega \subset \mathbb{R}^2$, what is the largest constant $\alpha = \alpha(\Omega) arXiv:1707.02418v1 [cs.GT] 8 Jul 2017 STABILITY, FAIRNESS AND RANDOM WALKS IN THE BARGAINING PROBLEM JAKOB KAPELLER AND STEFAN STEINERBERGER Abstract. We study the classical bargaining problem and its two canonical solutions, (Nash and Kalai-Smorodinsky), from a novel point of view: we ask for stability of the solution if both View linderman&steinerberger.pdf from MATH 6740 at Cornell University. CLUSTERING WITH T-SNE, PROVABLY. arXiv:1706.02582v1 [cs.LG] 8 Jun 2017 GEORGE C. LINDERMAN AND STEFAN STEINERBERGER Abstract.

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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

(with O r Lindenbaum), Re ned Least Squares for Support Recovery, arXiv:2103.10949 147 We study the problem of exact support recovery based on noisy observations and present Refined Least Squares (RLS).