Demo Data from EDBT 2026 / reviewers in the wild / expert
Chris Lauren
dblp:273/0104
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% | |
| Artificial intelligence
1 paper |
Deep learning architectures and training · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › release engineering
continuous integration |
0.4 | 1 | 2020 | Building Continuous Integration Services for Machine Learning · KDD 2020 |
Methods — techniques the papers use, named apart from their topics
continuous integration · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Building Continuous Integration Services for Machine LearningabstractContinuous integration (CI) has been a de facto standard for building industrial-strength software. Yet, there is little attention towards applying CI to the development of machine learning (ML) applications until the very recent effort on the theoretical side. In this paper, we take a step forward to bring the theory into practice. Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu 0001, Ce Zhang 0001, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer |
KDD | 8 |