Demo Data from SIGIR 2026 / reviewers in the wild / expert
Richard M. Dudley
dblp:72/6890
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › computational learning theory
learnability |
0.0 | 1 | 1994 | A metric entropy bound is not sufficient for learnability · IEEE Trans. Inf. Theory 1994 |
Machine learning › Learning theory
metric entropy |
0.0 | 1 | 1994 | A metric entropy bound is not sufficient for learnability · IEEE Trans. Inf. Theory 1994 |
Machine learning › Learning theory
PAC learning |
0.0 | 1 | 1994 | A metric entropy bound is not sufficient for learnability · IEEE Trans. Inf. Theory 1994 |
Methods — techniques the papers use, named apart from their topics
counterexample construction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | A metric entropy bound is not sufficient for learnabilityabstractThe authors prove by means of a counterexample that it is not sufficient, for probably approximately correct (PAC) learning under a class of distributions, to have a uniform bound on the metric entropy of the class of concepts to be learned. This settles a conjecture of Benedek and Itai (1991).> Richard M. Dudley, Sanjeev R. Kulkarni, T. J. Richardson, Ofer Zeitouni |
IEEE Trans. Inf. Theory | 1 |