3 papers
cs.LG2021
Towards Handling Uncertainty-at-Source in AI -- A Review and Next Steps for Interval Regression
Shaily Kabir, Christian Wagner, Zack Ellerby
Most of statistics and AI draw insights through modelling discord or variance between sources of information (i.e., inter-source uncertainty). Increasingly, however, research is fo…
cs.HC2020
Capturing Richer Information -- On Establishing the Validity of an Interval-Valued Survey Response Mode
Zack Ellerby, Christian Wagner, Stephen Broomell
Obtaining quantitative survey responses that are both accurate and informative is crucial to a wide range of fields. Traditional and ubiquitous response formats such as Likert and…
cs.CR2019
Exploring how Component Factors and their Uncertainty Affect Judgements of Risk in Cyber-Security
Zack Ellerby, Josie McCulloch, Melanie Wilson +1
Subjective judgements from experts provide essential information when assessing and modelling threats in respect to cyber-physical systems. For example, the vulnerability of indivi…