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

Conner Flansburg

dblp:324/1645 · DBLP profile ↗
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1ranked-venue papers
1as 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 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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
Probabilistic and Bayesian machine learning · 33% Optimization for machine learning · 33% Trustworthy machine learning · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
regression		 0.612022
Wind Prediction under Random Data Corruption (Student Abstract) · AAAI 2022
Machine learning › Optimization for machine learning › regularized risk minimization
regularized regression		 0.612022
Wind Prediction under Random Data Corruption (Student Abstract) · AAAI 2022
Machine learning › Trustworthy machine learning
robustness		 0.612022
Wind Prediction under Random Data Corruption (Student Abstract) · AAAI 2022

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

ridge regression · 0.6neural network · 0.6lasso regression · 0.6
YearPublicationVenuePosition
2022 Wind Prediction under Random Data Corruption (Student Abstract)
abstract
We study the robustness of ridge regression, lasso regression, and of a neural network, when the training set has been randomly corrupted and in response to this corruption the training-size is reduced in order to remove the corrupted data. While the neural network appears to be the most robust method among these three, nevertheless lasso regression appears to be the method of choice since it suffers less loss both when the full information is available to the learner, as well as when a significant amount of the original training set has been rendered useless because of random data corruption.
Conner Flansburg, Dimitrios I. Diochnos
 AAAI1