Demo Data from EDBT 2026 / reviewers in the wild / expert
Christopher Inskip
dblp:256/6222
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-3276-7498ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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 |
Efficient and distributed learning · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning › model reuse
model patching |
0.6 | 1 | 2022 | RealPatch: A Statistical Matching Framework for Model Patching with Real Samples · ECCV (25) 2022 |
Methods — techniques the papers use, named apart from their topics
statistical matching · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | RealPatch: A Statistical Matching Framework for Model Patching with Real Samples
Sara Romiti, Christopher Inskip, Viktoriia Sharmanska, Novi Quadrianto |
ECCV (25) | 2 |
| 2019 | Quantification under class-conditional dataset shiftabstractQuantification is the estimation of class proportions in a dataset. It is important in a wide range of fields, such as the proportion of positive reviews in sentiment analysis or the age and gender distribution of respondents in market research. David Spence, Christopher Inskip, Novi Quadrianto, David Weir |
ASONAM | 2 |