VLDB 2026 Research / reviewers in the wild / expert
Jan Sprenger
dblp:25/4572
· DBLP profile ↗
7ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-0083-9685ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5Applied, interdisciplinary, general and emerging computing · 5Theory of computation · 2 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Causal modeling semantics for counterfactuals with disjunctive antecedents
Giuliano Rosella, Jan Sprenger |
Ann. Pure Appl. Log. | 2 |
| 2017 | Determinants of judgments of explanatory power: Credibility, Generalizability, and Causal Framing
Matteo Colombo, Leandra Bucher, Jan Sprenger |
CogSci | 3 |
| 2016 | Explanatory Value, Probability, and Abductive Inference
Matteo Colombo, Marie Postma, Jan Sprenger |
CogSci | 3 |
| 2016 | The Influence of Language-specific Auditory Cues on the Learnability of Center-embedded Recursion
Jun Lai, Chiara de Jong, Dingguo Gao, Ren Huang, Emiel Krahmer, Jan Sprenger |
CogSci | 6 |
| 2015 | The learnability of Auditory Center-embedded Recursion
Jun Lai, Emiel Krahmer, Jan Sprenger |
CogSci | 3 |
| 2014 | Studying Frequency Effects in Learning Center-embedded Recursion
Jun Lai, Emiel Krahmer, Jan Sprenger |
CogSci | 3 |
| 2010 | Reliable Methods of Judgement AggregationabstractThe aggregation of consistent individual judgements on logically interconnected propositions into a collective judgement on the same propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgement aggregation refers to such a problem as the discursive dilemma. In this article we assume that the decision which the group is trying to reach is factually right or wrong. Hence, we address the question of how good various approaches are at selecting the right conclusion. We focus on two approaches: distance-based procedures and a Bayesian analysis. They correspond to group-internal and group external decision making, respectively. We compare those methods in a probabilistic model whose assumptions are subsequently relaxed. Our findings have two general implications for judgement aggregation problems: first, in a voting procedure, reasons should carry higher weight than the conclusion, and second, considering members of an advisory board to be highly competent is a better strategy than discounting their advice. Stephan Hartmann 0001, Gabriella Pigozzi, Jan Sprenger |
J. Log. Comput. | 3 |