EDBT 2026 Demo / reviewers in the wild / expert
Erwin Bolthausen
dblp:323/5804
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › online learning
perceptron |
0.6 | 1 | 2022 | Gardner formula for Ising perceptron models at small densities · COLT 2022 |
Machine learning › Learning theory › statistical learning theory
statistical physics of learning |
0.6 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Gardner formula for Ising perceptron models at small densitiesabstractWe 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 |
COLT | 1 |