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Guido Montúfar

Portrait of Guido Montúfar
Research Group Leader

Guido Montúfar

Research Group Leader
Email - 731 E2 05
Mathematical Machine Learning MiS Profile MiS Profile
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Research

My research interests are:

  • Deep Learning Theory
  • Mathematical Machine Learning
  • Graphical Models
  • Information Geometry
  • Algebraic Statistics

Assistant Professor, Departments of Mathematics and Statistics, UCLA (since 07/2017)

Research Group Leader, Mathematical Machine Learning Group, Max Planck Institute for Mathematics in the Sciences (since 07/2018)

Postdoc, Max Planck Institute for Mathematics in the Sciences, Information Theory of Cognitive Systems Group (06/2013-09/2017)

Research Associate, Department of Mathematics, Pennsylvania State University (02/2012-05/2013)

PhD in Mathematics, MPI MIS, Leipzig University (10/2012)

Research Assistant, Institute for Theoretical Physics, TU-Berlin (03/2008-12/2008)

Diplom Physiker, TU-Berlin (12/2008)

Teaching Assistant, Institute for Mathematics, TU-Berlin (03/2006-02/2008)

Diplom Mathematiker, TU-Berlin (08/2007)

Publications

Preprint
2024
Marie-Charlotte Brandenburg et al.

The real tropical geometry of neural networks for binary classification

(Transactions on machine learning research)

BibTex

 ArXiv: 2403.11871 Link: openreview.net
inJournal
2024
Mareike Dressler et al.

Algebraic optimization of sequential decision problems

In: Journal of symbolic computation

BibTex

 DOI: 10.1016/j.jsc.2023.102241 ArXiv: 2211.09439
Preprint
2024
Kedar Karhadkar et al.

Benign overfitting in leaky ReLU networks with moderate input dimension

BibTex

 ArXiv: 2403.06903
Preprint
2024
Kedar Karhadkar et al.

Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension

BibTex

 ArXiv: 2405.14630
Preprint
2024
Kedar Karhadkar et al.

Mildly overparameterized ReLU networks have a favorable loss landscape

(Transactions on machine learning research)

BibTex

 ArXiv: 2305.19510 Link: openreview.net
inJournal
2024
Kathlén Kohn et al.

Function space and critical points of linear convolutional networks

In: SIAM journal on applied algebra and geometry

BibTex

 ArXiv: 2304.05752
Preprint
2024
Shuang Liang et al.

Pull-back geometry of persistent homology encodings

(Transactions on machine learning research)

BibTex

 ArXiv: 2310.07073 Link: openreview.net
Preprint
2024
Johannes Müller et al.

Fisher-Rao gradient flows of linear programs and state-action natural policy gradients

BibTex

 ArXiv: 2403.19448
inJournal
2024
Johannes Müller et al.

Geometry and convergence of natural policy gradient methods

In: Information geometry

BibTex

 DOI: 10.1007/s41884-023-00106-z ArXiv: 2211.02105

MiS Preprint
inBook
2023
Pierre Brechet et al.

Critical points and convergence analysis of generative deep linear networks trained with Bures-Wasserstein loss

In: Proceedings of the 40th international conference on machine learning : , 23-29 July 2023, Honolulu, Hawaii, USA

BibTex

 ArXiv: 2303.03027 Link: proceedings.mlr.press
inJournal
2023
Hui Jin et al.

Implicit bias of gradient descent for mean squared error regression with wide neural networks

In: Journal of machine learning research

BibTex

 ArXiv: 2006.07356

MiS Preprint
inBook
2023
Thomas Merkh et al.

Stochastic feedforward neural networks : universal approximation

In: Mathematical aspects of deep learning

BibTex

 DOI: 10.1017/9781009025096.007 ArXiv: 1910.09763
All Personal Publications
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