Visual analytics for collaborative human-machine confidence in human-centric active learning tasks

Human-Centric Justification of Machine Learning Predictions

Human decision makers in many domains can make use of predictions made by machine learning models in their decision making process, but the usability of these predictions is limited if the human is unable to justify his or her trust in the prediction. We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowl...

Collaborative Visual Analytics 1 COLLABORATIVE VISUAL ANALYTICS

Transductive Confidence Machine for Active Learning

This paper describes a novel active learning strategy using universal p-value measures of confidence based on algorithmic randomness, and transductive inference. The early stopping criteria for active learning is based on the bias-variance tradeoff for classification. This corresponds to that learning instance when the boundary bias becomes positive, and requires one to switch from active to ra...

Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks

Human behavior understanding is arguably one of the most important mid-level components in artificial intelligence. In order to efficiently make use of data, multi-task learning has been studied in diverse computer vision tasks including human behavior understanding. However, multitask learning relies on task specific datasets and constructing such datasets can be cumbersome. It requires huge a...

Using Collaborative Visual Analytics for Innovative Industry-inspired Learning Activities

Inspired by leading industry practices, this paper describes an innovative learning activity designed to combine data visualisation and cross-functional collaboration supported by enterprise social media. The activity is structured around sharing, co-creation and negotiation of departmental/disciplinary insights across data silos, using both internal and external data. In addition to giving stu...