Results 31 to 40 of about 6,437 (212)
Mental Simulation as a Remedy for Biased Reasoning [PDF]
Studia Psychologica, 2019 Prompting mental simulation with a counterfactual scenario has been found to enhance rationality in individuals and groups. Building upon previous findings and the dual-process accounts of reasoning, we hypothesized that debiasing power of mental ...
Daša Strachanová, Lenka Valuš
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Experts’ Failure to Consider the Negative Predictive Power of Symptom Validity Tests
Frontiers in Psychology, 2022 Feigning (i.e., grossly exaggerating or fabricating) symptoms distorts diagnostic evaluations. Therefore, dedicated tools known as symptom validity tests (SVTs) have been developed to help clinicians differentiate feigned from genuine symptom ...
Isabella J. M. Niesten +5 more
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Debiased recommendation with neural stratification
AI Open, 2022 Debiased recommender models have recently attracted increasing attention from the academic and industry communities. Existing models are mostly based on the technique of inverse propensity score (IPS). However, in the recommendation domain, IPS can be hard to estimate given the sparse and noisy nature of the observed user-item exposure data.
Quanyu Dai, Zhenhua Dong, Xu Chen
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Debiasing Evaluations That Are Biased by Evaluations
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 It is common to evaluate a set of items by soliciting people to rate them. For example, universities ask students to rate the teaching quality of their instructors, and conference organizers ask authors of submissions to evaluate the quality of the reviews. However, in these applications, students often give a higher rating to a course if they receive
Jingyan Wang 0001 +3 more
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Adversarial Generative Multi-sensitive Attribute Data Biasing Method [PDF]
Jisuanji kexueThis paper proposes a method for multi-sensitive attribute data debiasing,leveraging adversarial learning and autoencoder to eliminate correlations between sensitive and non-sensitive attributes,minimize the impact on model accuracy when striving for ...
WANG Wenpeng, GE Hongwei, LI Ting
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Cognitive Errors and Debiasing. [PDF]
J Educ Teach Emerg MedAlthough this lecture was given to first-year residents, it is also appropriate for upper-level residents, medical students, fellows, and faculty.Medical errors are largely due to errors of cognition rather than lack of knowledge.1 The cognitive processes that underlie these errors are often explained using Dual Process Theory, which posits that we ...
Ginsburg J.
europepmc +4 more sources
Debiasing Algorithms for Protein Ligand Binding Data do not Improve Generalisation [PDF]
, 2019 The structured nature of chemical data means machine learning models trained to predict protein-ligand binding risk overfitting the data, impairing their ability to generalise and make accurate predictions for novel candidate ligands.
Lucy, Colwell +3 more
core +1 more source
CoRR, 2020 Entropy regularization in optimal transport (OT) has been the driver of many recent interests for Wasserstein metrics and barycenters in machine learning. It allows to keep the appealing geometrical properties of the unregularized Wasserstein distance while having a significantly lower complexity thanks to Sinkhorn's algorithm.
Janati, Hicham +2 more
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Debiasing the debiased Lasso with bootstrap
Electronic Journal of Statistics, 2020 Accepted ...
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CoRR, 2020 A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar (negative) points are typically taken to be randomly sampled datapoints, implicitly accepting that these points may, in reality, actually have the same label.
Ching-Yao Chuang +4 more
openaire +3 more sources