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Demo Data from EDBT 2026 / reviewers in the wild / expert

Nitesh Sekhar

dblp:335/1885 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—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
Trustworthy machine learning · 50% Deep learning architectures and training · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › loss function design
logit adjustment		 0.712023
Avoiding spurious correlations via logit correction · ICLR 2023
Machine learning › Trustworthy machine learning › robustness › spurious correlation
spurious correlation mitigation		 0.712023
Avoiding spurious correlations via logit correction · ICLR 2023

Methods — techniques the papers use, named apart from their topics

logit adjustment · 0.7
YearPublicationVenuePosition
2023 Avoiding spurious correlations via logit correction
Nitesh Sekhar, Prateek Singhal, Carlos Fernandez-Granda
 ICLR3