EDBT 2026 Demo / reviewers in the wild / expert
Allison Berke
dblp:32/6429
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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 |
Probabilistic and Bayesian machine learning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities
cognitive science |
0.1 | 1 | 2006 | Combining causal and similarity-based reasoning · NIPS 2006 |
Methods — techniques the papers use, named apart from their topics
bayesian modeling · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Combining causal and similarity-based reasoningabstractEveryday inductive reasoning draws on many kinds of knowledge, including knowledge about relationships between properties and knowledge about relationships between objects. Previous accounts of inductive reasoning generally focus on just one kind of knowledge: models of causal reasoning often focus on relationships between properties, and models of similarity-based reasoning often focus on similarity relationships between objects. We present a Bayesian model of inductive reasoning that incorporates both kinds of knowledge, and show that it accounts well for human inferences about the properties of biological species. Charles Kemp, Patrick Shafto, Allison Berke, Josh Tenenbaum |
NIPS | 3 |