no code implementations • 8 Nov 2022 • Markus Kängsepp, Meelis Kull
In driving scenarios with poor visibility or occlusions, it is important that the autonomous vehicle would take into account all the uncertainties when making driving decisions, including choice of a safe speed.
1 code implementation • 16 Mar 2022 • Markus Kängsepp, Kaspar Valk, Meelis Kull
This motivates the fit-on-the-test view on evaluation: first, approximate a calibration map on the test data, and second, quantify its distance from the identity.
2 code implementations • 14 Mar 2022 • Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim
While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences.
no code implementations • 20 Dec 2021 • Telmo Silva Filho, Hao Song, Miquel Perello-Nieto, Raul Santos-Rodriguez, Meelis Kull, Peter Flach
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration.
no code implementations • 3 Nov 2021 • Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull
Minimizing expected loss measured by a proper scoring rule, such as Brier score or log-loss (cross-entropy), is a common objective while training a probabilistic classifier.
1 code implementation • 11 Oct 2021 • Mohamed Maher, Meelis Kull
Label smoothing is widely used in deep neural networks for multi-class classification.
1 code implementation • 28 Mar 2020 • Anti Ingel, Novin Shahroudi, Markus Kängsepp, Andre Tättar, Viacheslav Komisarenko, Meelis Kull
We participated in the M4 competition for time series forecasting and describe here our methods for forecasting daily time series.
1 code implementation • NeurIPS 2019 • Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence.
3 code implementations • 28 Oct 2019 • Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence.
1 code implementation • 7 Aug 2019 • Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perello-Nieto, Emma Tonkin, Peter Flach
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities.
no code implementations • 15 May 2019 • Hao Song, Tom Diethe, Meelis Kull, Peter Flach
We are concerned with obtaining well-calibrated output distributions from regression models.
no code implementations • 20 Jun 2018 • 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.
no code implementations • 4 Feb 2017 • Tom Diethe, Niall Twomey, Meelis Kull, Peter Flach, Ian Craddock
There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home.
1 code implementation • NeurIPS 2015 • Peter Flach, Meelis Kull
Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier's performance.