Publications related to Selection bias

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Frédéric Courbin, Georges Meylan, Gianluca Castignani, Maurizio Martinelli, Matthias Wiesmann, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina

Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for gala ...

Edp Sciences S A2024

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Matthias Grossglauser, Aswin Suresh, Chi Hsuan Wu

We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

2024

Euclid: Improving the efficiency of weak lensing shear bias calibration

Frédéric Courbin, Georges Meylan, Malte Tewes, Yi Wang, Richard Massey, Marcello Farina

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. As it typically requires millions of simulated galaxy images and ...

Edp Sciences S A2024

Exploiting the Signal-Leak Bias in Diffusion Models

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert, Athanasios Fitsios

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

2024

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Mats Julius Stensrud

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus, and codeine has no effect on pain in individuals with certain polym ...

Lippincott Williams & Wilkins2024

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Timing and spatial selection bias in rapid extreme event attribution

Anthony Christopher Davison, Ophélia Mireille Anna Miralles

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

ELSEVIER2023

Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality

David Pascucci

Visual estimates of stimulus features are systematically biased toward the features of previously encountered stimuli. Such serial dependencies have often been linked to how the brain maintains perceptual continuity. However, serial dependence has mostly b ...

ASSOC RESEARCH VISION OPHTHALMOLOGY INC2023

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Selection bias

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Euclid: Improving the efficiency of weak lensing shear bias calibration

Exploiting the Signal-Leak Bias in Diffusion Models

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Timing and spatial selection bias in rapid extreme event attribution

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality

Graph Chatbot

Chat with Graph Search

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Exploiting the Signal-Leak Bias in Diffusion Models

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Timing and spatial selection bias in rapid extreme event attribution

Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Euclid: Improving the efficiency of weak lensing shear bias calibration