Explorations in the Mathematics of Data Science

• Titel: Explorations in the Mathematics of Data Science
• Titelzusatz: The Inaugural Volume of the Center for Approximation and Mathematical Data Analytics
• Mitwirkende: Foucart, Simon [HerausgeberIn]
• Mitwirkende: Wojtowytsch, Stephan [HerausgeberIn]
• Verf.angabe: edited by Simon Foucart, Stephan Wojtowytsch
• Ausgabe: 1st ed. 2024.
• Verlagsort: Cham
• Verlagsort: Cham
• Verlag: Springer Nature Switzerland
• Verlag: Imprint: Birkhäuser
• E-Jahr: 2024
• Jahr: 2024.
• Jahr: 2024.
• Umfang: 1 Online-Ressource(XIV, 286 p. 24 illus., 20 illus. in color.)
• Gesamttitel/Reihe: Applied and Numerical Harmonic Analysis
• ISBN: 978-3-031-66497-7
• DOI: doi:10.1007/978-3-031-66497-7
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: CAMDA Conference (1. : 2023 : College Station): Explorations in the mathematics of data science. - Cham : Birkhäuser, 2024. - xiv, 286 Seiten
• K10plus-PPN: 1902720881
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Preface -- S-Procedure Relaxation: a Case of Exactness Involving Chebyshev Centers -- Neural networks: deep, shallow, or in between? -- Qualitative neural network approximation over R and C -- Linearly Embedding Sparse Vectors from l2 to l1 via Deterministic Dimension-Reducing Maps -- Ridge Function Machines -- Learning Collective Behaviors from Observation -- Provably Accelerating Ill-Conditioned Low-Rank Estimation via Scaled Gradient Descent, Even with Overparameterization -- CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D -- A genomic tree based sparse solver -- A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces.

Abstract

This edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA’s inaugural conference – held in May 2023 – and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory.