Zobrazeno 1 - 10
of 535
pro vyhledávání: '"Rundensteiner, Elke"'
As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demogra
Externí odkaz:
http://arxiv.org/abs/2407.17459
Publikováno v:
ICDE 2024
The generation and collection of big data series are becoming an integral part of many emerging applications in sciences, IoT, finance, and web applications among several others. The terabyte-scale of data series has motivated recent efforts to desig
Externí odkaz:
http://arxiv.org/abs/2404.09637
Deep neural networks (DNNs) have advanced many machine learning tasks, but their performance is often harmed by noisy labels in real-world data. Addressing this, we introduce CoLafier, a novel approach that uses Local Intrinsic Dimensionality (LID) f
Externí odkaz:
http://arxiv.org/abs/2401.05458
Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive huma
Externí odkaz:
http://arxiv.org/abs/2312.01225
Publikováno v:
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 1685-1698)
For applications where multiple stakeholders provide recommendations, a fair consensus ranking must not only ensure that the preferences of rankers are well represented, but must also mitigate disadvantages among socio-demographic groups in the final
Externí odkaz:
http://arxiv.org/abs/2308.06233
Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals. However, for many classification problems, only small portions of long time series are often rele
Externí odkaz:
http://arxiv.org/abs/2302.04052
Explainability helps users trust deep learning solutions for time series classification. However, existing explainability methods for multi-class time series classifiers focus on one class at a time, ignoring relationships between the classes. Instea
Externí odkaz:
http://arxiv.org/abs/2210.05411
Autor:
Hartvigsen, Thomas, Gerych, Walter, Thadajarassiri, Jidapa, Kong, Xiangnan, Rundensteiner, Elke
Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their patients' outcomes by accurately predicting infections. While early c
Externí odkaz:
http://arxiv.org/abs/2208.09795
Combining the preferences of many rankers into one single consensus ranking is critical for consequential applications from hiring and admissions to lending. While group fairness has been extensively studied for classification, group fairness in rank
Externí odkaz:
http://arxiv.org/abs/2207.10020
Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generatin
Externí odkaz:
http://arxiv.org/abs/2207.07765