290 citations · 1.2k across the 29 of their papers we have counts for
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cs.LG2021★ 194 cited
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
Telmo Silva Filho, Hao Song, Miquel Perello-Nieto +3
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the…
cs.LG2021
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning
Stefan Radic Webster, Peter Flach
Identifying uncertainty and taking mitigating actions is crucial for safe and trustworthy reinforcement learning agents, especially when deployed in high-risk environments. In this…