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pro vyhledávání: '"Kelly, Markelle"'
Uncertainty expressions such as ``probably'' or ``highly unlikely'' are pervasive in human language. While prior work has established that there is population-level agreement in terms of how humans interpret these expressions, there has been little i
Externí odkaz:
http://arxiv.org/abs/2407.15814
Improving our understanding of how humans perceive AI teammates is an important foundation for our general understanding of human-AI teams. Extending relevant work from cognitive science, we propose a framework based on item response theory for model
Externí odkaz:
http://arxiv.org/abs/2305.09064
Autor:
Kelly, Markelle, Smyth, Padhraic
The deployment of machine learning classifiers in high-stakes domains requires well-calibrated confidence scores for model predictions. In this paper we introduce the notion of variable-based calibration to characterize calibration properties of a mo
Externí odkaz:
http://arxiv.org/abs/2209.15154