690 citations
- University of North Carolina at Chapel HillUS24 papers
- Lawrence Berkeley National LaboratoryUS7 papers
- Johns Hopkins UniversityUS4 papers
- University of Colorado AnschutzUS4 papers
- University of FloridaUS4 papers
- European Bioinformatics InstituteGB3 papers
- European Molecular Biology LaboratoryDE3 papers
- Harvard UniversityUS3 papers
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- University of ChicagoUS3 papers
- University of WashingtonUS3 papers
6 papers · 1 filter
Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis
Arran Zeyu Wang, David Borland, David Gotz
Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualiza…
Causal Priors and Their Influence on Judgements of Causality in Visualized Data
Arran Zeyu Wang, David Borland, Tabitha C. Peck +2
"Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlati…
Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach
Fernando Martinez, Mariyam Mapkar, Ali Alfatemi +4
Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learnin…
The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization
Kathleen R. Mullen, Imke Tammen, Nicolas A. Matentzoglu +13
Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the app…
An Empirical Study of Counterfactual Visualization to Support Visual Causal Inference
Arran Zeyu Wang, David Borland, David Gotz
Counterfactuals -- expressing what might have been true under different circumstances -- have been widely applied in statistics and machine learning to help understand causal relat…
Using Counterfactuals to Improve Causal Inferences from Visualizations
David Borland, Arran Zeyu Wang, David Gotz
Traditional approaches to data visualization have often focused on comparing different subsets of data, and this is reflected in the many techniques developed and evaluated over th…