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Erwin Bolthausen

dblp:323/5804 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Learning theory · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › online learning
perceptron
0.612022
Gardner formula for Ising perceptron models at small densities · COLT 2022
Machine learning › Learning theory › statistical learning theory
statistical physics of learning
0.612022
Gardner formula for Ising perceptron models at small densities · COLT 2022

Methods — techniques the papers use, named apart from their topics

moment methods · 0.6concentration · 0.6approximate message passing · 0.6
YearPublicationVenuePosition
2022 Gardner formula for Ising perceptron models at small densities
abstract
We consider the Ising perceptron model with N spins and M = N*alpha patterns, with a general activation function U that is bounded above. For U bounded away from zero, or U a one-sided threshold function, it was shown by Talagrand (2000, 2011) that for small densities alpha, the free energy of the model converges in the large-N limit to the replica symmetric formula conjectured in the physics literature (Krauth–Mezard 1989, see also Gardner–Derrida 1988). We give a new proof of this result, which covers the more general class of all functions U that are bounded above and satisfy a certain variance bound. The proof uses the (first and second) moment method conditional on the approximate message passing iterates of the model. In order to deduce our main theorem, we also prove a new concentration result for the perceptron model in the case where U is not bounded away from zero.
Erwin Bolthausen, Shuta Nakajima, Nike Sun, Changji Xu
COLT1