Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael Timothy Bennett
dblp:290/8844
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 |
Efficient and distributed learning · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
data-efficient learning |
0.9 | 1 | 2025 | A Formal Theory of Optimal Learning with Experimental Results · IJCAI 2025 |
Machine learning › Efficient and distributed learning
energy-efficient learning |
0.3 | 1 | 2025 | A Formal Theory of Optimal Learning with Experimental Results · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
mathematical proof · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Formal Theory of Optimal Learning with Experimental ResultsabstractHere I summarise some results from my thesis which are relevant to artificial intelligence (AI) and machine learning (ML). The key contribution is a theory of optimally sample and energy efficient learning, which is supported by mathematical proofs and experimental results. Michael Timothy Bennett |
IJCAI | 1 |