194 citations · 424 across the 14 of their papers we have counts for
14 papers
Calibrating Perception Uncertainty for Autonomous Driving
Markus Kängsepp, Meelis Kull
Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception models in these systems perform…
On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers
Markus Kängsepp, Kaspar Valk, Meelis Kull
Every uncalibrated classifier has a corresponding true calibration map that calibrates its confidence. Deviations of this idealistic map from the identity map reveal miscalibration…
Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity
Kacper Sokol, Meelis Kull, Jeffrey Chan +1
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 a…
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…
Shift Happens: Adjusting Classifiers
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. If the…
Instance-based Label Smoothing For Better Calibrated Classification Networks
Mohamed Maher, Meelis Kull
Label smoothing is widely used in deep neural networks for multi-class classification. While it enhances model generalization and reduces overconfidence by aiming to lower the prob…
Correlated daily time series and forecasting in the M4 competition
Anti Ingel, Novin Shahroudi, Markus Kängsepp +3
We participated in the M4 competition for time series forecasting and describe here our methods for forecasting daily time series. We used an ensemble of five statistical forecasti…
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp +3
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperatu…
HyperStream: a Workflow Engine for Streaming Data
Tom Diethe, Meelis Kull, Niall Twomey +5
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabili…
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…
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…
CASP-DM: Context Aware Standard Process for Data Mining
Fernando Martínez-Plumed, Lidia Contreras-Ochando, Cèsar Ferri +5
We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and mod…
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…
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data
Niall Twomey, Tom Diethe, Meelis Kull +8
This paper outlines the Sensor Platform for HEalthcare in Residential Environment (SPHERE) project and details the SPHERE challenge that will take place in conjunction with Europea…