Scholar
Matus Telgarsky
Google Scholar ID: Fc-5yRIAAAAJ
Courant Institute of Mathematical Sciences, New York University
deep learning theory
machine learning theory
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Citations & Impact
All-time
Citations
5,597
H-index
23
i10-index
33
Publications
20
Co-authors
25
list available
Contact
GitHub
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Publications
2 items
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
2025
Cited
0
Convex Analysis at Infinity: An Introduction to Astral Space
arXiv.org · 2022
Cited
10
Resume (English only)
Academic Achievements
2013: Showed coordinate descent (and steepest descent) converges to maximum margin solutions
2016: Constructed deep networks inapproximable by shallow ones unless exponentially wide
2018: Extended classical margin-based generalization theory to deep networks
2019: Large-margin analysis of gradient descent for non-separable data, with a succinct SGD proof (1/t rate)
2020: Proved directional convergence of gradient descent for shallow and deep ReLU networks near initialization
2024: Interpreted (decoder-)Transformer layers as parallel computation rounds; showed logistic regression is insensitive to step size
Co-authors
25 total
Co-author 1
Daniel Hsu
Columbia University
Peter Bartlett
Professor, EECS and Statistics, UC Berkeley
Sham M Kakade
Harvard University
Anima Anandkumar
California Institute of Technology and NVIDIA
Co-author 6
Dylan J. Foster
Principal Researcher, Microsoft Research
Clayton Sanford
Google Research
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