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11/08/2022
Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View
In driving scenarios with poor visibility or occlusions, it is important...
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03/16/2022
On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers
Every uncalibrated classifier has a corresponding true calibration map t...
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03/14/2022
Ethical and Fairness Implications of Model Multiplicity
While predictive models are a purely technological feat, they may operat...
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12/20/2021
Classifier Calibration: How to assess and improve predicted class probabilities: a survey
This paper provides both an introduction to and a detailed overview of t...
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11/03/2021
Shift Happens: Adjusting Classifiers
Minimizing expected loss measured by a proper scoring rule, such as Brie...
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10/11/2021
Instance-based Label Smoothing For Better Calibrated Classification Networks
Label smoothing is widely used in deep neural networks for multi-class c...
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03/28/2020
Correlated daily time series and forecasting in the M4 competition
We participated in the M4 competition for time series forecasting and de...
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03/28/2020
Correlated daily time series and forecasting in M4 competition
We participated in the M4 competition for time series forecasting and de...
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10/28/2019
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Class probabilities predicted by most multiclass classifiers are uncalib...
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08/07/2019
HyperStream: a Workflow Engine for Streaming Data
This paper describes HyperStream, a large-scale, flexible and robust sof...
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05/15/2019
Distribution Calibration for Regression
We are concerned with obtaining well-calibrated output distributions fro...
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06/20/2018
Non-Parametric Calibration of Probabilistic Regression
The task of calibration is to retrospectively adjust the outputs from a ...
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09/19/2017
CASP-DM: Context Aware Standard Process for Data Mining
We propose an extension of the Cross Industry Standard Process for Data ...
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02/04/2017