162 citations · 209 across the 6 of their papers we have counts for
Showing stat.MLShow all
3 papers · 1 filter
stat.ML2019★ 9 cited
Distribution Calibration for Regression
Hao Song, Tom Diethe, Meelis Kull +1
We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding th…
stat.ML2018
Non-Parametric Calibration of Probabilistic Regression
Hao Song, Meelis Kull, Peter Flach
The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has…
stat.ML2017★ 12 cited
Probabilistic Sensor Fusion for Ambient Assisted Living
Tom Diethe, Niall Twomey, Meelis Kull +2
There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform…