What is Mathematical Methods for Arbitrary Data Sources?
The lecture series will collect talks on mathematical disciplines related to all kind of data, ranging from statistics and machine learning to model-based approaches and inverse problems. Each pair of talks will address a specific direction, e.g., a NoMADS session related to nonlocal approaches or a DeepMADS session related to deep learning.
The series is created in the spirit of the One World Series pioneered by the seminars in probability and PDE.
Using Zoom
For this online seminar we will use zoom as video service. Approximately 15 minutes prior to the beginning of the lecture, a zoom link will be provided on this website and via mailing list.
Mailing list
Please subscribe to our mailing list by filling this form.
Semester
Sommersemester 2020
Lehrenden
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aktualisiert
2020-04-21 13:04:14
Abonnements
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# 1Offener ZugangGabriel Peyré: Scaling Optimal Transport for High dimensional Learning2020-04-20 Sommersemester 20201Gabriel Peyré: Scaling Optimal Transport for High dimensional Learning2020-04-20 Sommersemester 2020Offener Zugang
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# 2Offener ZugangMarie-Therese Wolfram: Inverse Optimal Transport2020-04-20 Sommersemester 20202Marie-Therese Wolfram: Inverse Optimal Transport2020-04-20 Sommersemester 2020Offener Zugang
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# 3Offener ZugangLorenzo Rosasco: Efficient learning with random projections2020-05-04 Sommersemester 20203Lorenzo Rosasco: Efficient learning with random projections2020-05-04 Sommersemester 2020Offener Zugang
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# 4Offener ZugangMichaël Fanuel: Diversity sampling in kernel method2020-05-04 Sommersemester 20204Michaël Fanuel: Diversity sampling in kernel method2020-05-04 Sommersemester 2020Offener Zugang
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# 5Offener ZugangLars Ruthotto: Machine Learning meets Optimal Transport: Old solutions for new problems and vice versa2020-05-18 Sommersemester 20205Lars Ruthotto: Machine Learning meets Optimal Transport: Old solutions for new problems and vice versa2020-05-18 Sommersemester 2020Offener Zugang
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# 6Offener ZugangFrancis Bach: On the convergence of gradient descent for wide two-layer neural networks2020-05-18 Sommersemester 20206Francis Bach: On the convergence of gradient descent for wide two-layer neural networks2020-05-18 Sommersemester 2020Offener Zugang
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# 7Offener ZugangMichael Unser: Representer theorems for machine learning and inverse problems2020-06-08 Sommersemester 20207Michael Unser: Representer theorems for machine learning and inverse problems2020-06-08 Sommersemester 2020Offener Zugang
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# 8Offener ZugangVincent Duval: Representing the solutions of total variation regularized problems2020-06-08 Sommersemester 20208Vincent Duval: Representing the solutions of total variation regularized problems2020-06-08 Sommersemester 2020Offener Zugang
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# 9Offener ZugangAndrea Braides: Continuum limits of interfacial energies on (sparse and) dense graphs2020-06-15 Sommersemester 20209Andrea Braides: Continuum limits of interfacial energies on (sparse and) dense graphs2020-06-15 Sommersemester 2020Offener Zugang
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# 10Offener ZugangNicolás García Trillos: Regularity theory and uniform convergence in the large data limit of graph Laplacian eigenvectors on random data clouds2020-06-15 Sommersemester 202010Nicolás García Trillos: Regularity theory and uniform convergence in the large data limit of graph Laplacian eigenvectors on random data clouds2020-06-15 Sommersemester 2020Offener Zugang
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# 11Offener ZugangJana de Wiljes: Sequential learning for decision support under uncertainty2020-06-29 Sommersemester 202011Jana de Wiljes: Sequential learning for decision support under uncertainty2020-06-29 Sommersemester 2020Offener Zugang
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# 12Offener ZugangBjörn Sprungk: Noise-level robust Monte Carlo methods for Bayesian inference with infomative data2020-06-29 Sommersemester 202012Björn Sprungk: Noise-level robust Monte Carlo methods for Bayesian inference with infomative data2020-06-29 Sommersemester 2020Offener Zugang
