EDBT 2026 Demo / reviewers in the wild / expert
David G. Willis
dblp:22/39
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1970
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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.
| Theoretical computer science
1 paper |
Computational complexity · 87% Information theory · 13% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational complexity › algorithmic randomness
computable probability measures |
0.0 | 1 | 1970 | Computational Complexity and Probability Constructions · J. ACM 1970 |
Methods — techniques the papers use, named apart from their topics
universal turing machine · 0.0constructive correspondence · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1970 | Computational Complexity and Probability ConstructionsabstractThere exist constructive correspondences between Turing machines having finitely determinable behavior and computable probability measures on their output sequences.These correspondences determine limits on the relative accuracies with which different computable probability measures predict events.Using any universal Turing machine as a basis, it is possible to construct an infinite hierarchy of increasingly accurate computable probability measures which are independent of any probability assumptions.The relationship of such measures to real events is considered. David G. Willis |
J. ACM | 1 |