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Fairness through Aleatoric Uncertainty [PDF]
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023 We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group or individual, utility focuses on maximizing the model's predictive performance.
Anique Tahir +2 more
openaire +4 more sources
Aleatoric and Epistemic Uncertainty with Random Forests [PDF]
, 2020 10 pages, 4 ...
Shaker, Mohammad Hossein +1 more
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Grounding Aleatoric Uncertainty for Unsupervised Environment Design [PDF]
Advances in Neural Information Processing Systems 35, 2022 NeurIPS ...
Minqi Jiang +7 more
core +8 more sources
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning [PDF]
CoRR, 2022 Characterizing aleatoric and epistemic uncertainty on the predicted rewards can help in building reliable reinforcement learning (RL) systems. Aleatoric uncertainty results from the irreducible environment stochasticity leading to inherently risky states and actions. Epistemic uncertainty results from the limited information accumulated during learning
Bertrand Charpentier +3 more
core +5 more sources
Roland Barthes' "Text" and aleatoric music: Is "The birth of the reader" the birth of the listener? [PDF]
Muzikologija, 2002 The history of Western classical music and the development of its notational system show that composers have tried to control more and more aspects of their compositions as precisely as possible.
Jeongwon Joe, Song Hoo S.
doaj +3 more sources
Aleatoric and Epistemic Uncertainty in Conformal Prediction. [PDF]
Recently, there has been a particular interest in distinguishing different types of uncertainty in supervised machine learning (ML) settings (Hullermeier and Waegeman, 2021). Aleatoric uncertainty captures the inherent randomness in the data-generating process.
Sale, Yusuf +2 more
core +5 more sources
One Step Closer to Unbiased Aleatoric Uncertainty Estimation
Proceedings of the AAAI Conference on Artificial IntelligenceNeural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty.
Wang Zhang +6 more
openaire +5 more sources
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling [PDF]
, 2023 In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. To this end, we propose an ensemble of Normalizing Flows (NF), which are state-of-the-art
Meger, David, Berry, Lucas
core +3 more sources
MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 The ability to identify erroneous depth estimates is of fundamental interest. Information regarding the aleatoric uncertainty of depth estimates can be, for example, used to support the process of depth reconstruction itself.
Z. Zhong, M. Mehltretter
doaj +1 more source