Demo Data from SIGIR 2026 / reviewers in the wild / expert
Haruo Kakehi
dblp:298/7280
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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 |
Knowledge representation and reasoning · 50% Probabilistic and Bayesian machine learning · 50% | |
| Theoretical computer science
1 paper |
Information theory · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › causal reasoning
causal learning |
0.6 | 1 | 2022 | Learning Causal Models from Conditional Moment Restrictions by Importance Weighting · ICLR 2022 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
conditional moment restrictions |
0.6 | 1 | 2022 | Learning Causal Models from Conditional Moment Restrictions by Importance Weighting · ICLR 2022 |
Information theory
moment constraints |
0.2 | 1 | 2022 | Learning Causal Models from Conditional Moment Restrictions by Importance Weighting · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
importance weighting · 1.1instrumental variables · 0.6instrumental variable · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi |
ICLR | 5 |