Search Results for author: Meelis Kull

Found 14 papers, 7 papers with code

Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View

no code implementations8 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.

Semantic Segmentation

On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers

1 code implementation16 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.

Fairness and Ethics Under Model Multiplicity in Machine Learning

1 code implementation14 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.

Ethics Fairness

Shift Happens: Adjusting Classifiers

no code implementations3 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.

Correlated daily time series and forecasting in the M4 competition

1 code implementation28 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.

Time Series Forecasting

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

3 code implementations28 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.

HyperStream: a Workflow Engine for Streaming Data

1 code implementation7 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.

BIG-bench Machine Learning

Distribution Calibration for Regression

no code implementations15 May 2019 Hao Song, Tom Diethe, Meelis Kull, Peter Flach

We are concerned with obtaining well-calibrated output distributions from regression models.

Gaussian Processes regression

Non-Parametric Calibration of Probabilistic Regression

no code implementations20 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.

General Classification regression

Probabilistic Sensor Fusion for Ambient Assisted Living

no code implementations4 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.

Activity Recognition

Precision-Recall-Gain Curves: PR Analysis Done Right

no code implementations 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.

Model Selection

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