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Removing the Influence of Group Variables in High-Dimensional Predictive Modelling
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Effect of Brain Structure, Brain Function, and Brain Connectivity on Relapse in Alcohol-Dependent Patients
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Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review
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Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate
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Singular Value Decomposition and Principal Component Analysis
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Auditing Black-Box Models for Indirect Influence
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The use of transcranial magnetic stimulation in cognitive neuroscience: A new synthesis of methodological issues
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Tensor network factorizations: Relationships between brain structural connectomes and traits
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Statistics in the big data era: Failures of the machine
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The Neurocircuitry of Impaired Insight in Drug Addiction
inferredcites →https://doi.org/10.1038/nn.4361openalex-snapshot
The Human Connectome Project's neuroimaging approach
inferredcites →https://doi.org/10.1038/tpj.2010.57openalex-snapshot
A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data
inferredcites →https://doi.org/10.1073/pnas.97.18.10101openalex-snapshot
Singular value decomposition for genome-wide expression data processing and modeling
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Batch effect removal methods for microarray gene expression data integration: a survey
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Adjustment of systematic microarray data biases
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The sva package for removing batch effects and other unwanted variation in high-throughput experiments
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Adjusting batch effects in microarray expression data using empirical Bayes methods
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A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
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Law, Social Standing and Racial Disparities in Imprisonment
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Relationships between Human Brain Structural Connectomes and Traits
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Pattern Recognition and Machine Learning
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Classifying without discriminating
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Statistical Learning with Sparsity: The Lasso and Generalizations
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1145/2783258.2783311openalex-snapshot
Certifying and Removing Disparate Impact
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Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
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Mitigating Unwanted Biases with Adversarial Learning
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Algorithmic Fairness: Choices, Assumptions, and Definitions
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Fairness in Criminal Justice Risk Assessments: The State of the Art
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Orthogonal projections to latent structures as a strategy for microarray data normalization
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A Modified Principal Component Technique Based on the LASSO
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Sparse Principal Component Analysis
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Bayesian Inference and Testing of Group Differences in Brain Networks
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The problem of infra-marginality in outcome tests for discrimination
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An algorithm for removing sensitive information: Application to race-independent recidivism prediction
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Normal Multivariate Analysis and the Orthogonal Group
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Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
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Capturing Heterogeneity in Gene Expression Studies by "Surrogate Variable Analysis"
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
inferredcites →https://doi.org/10.48550/arxiv.1610.02413openalex-snapshot
Equality of Opportunity in Supervised Learning
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Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
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The Measure and Mismeasure of Fairness
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Singular Value Decomposition and Principal Component Analysis
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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From order to species: Modelling Amphipoda distributions in the Hamersley Range
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa189openalex-snapshot
Projected t-SNE for batch correction
inferred← citeshttps://doi.org/10.1093/biostatistics/kxac031openalex-snapshot
Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables
inferred← citeshttps://doi.org/10.1093/jrsssa/qnad020openalex-snapshot
Victor Richmond Jose's contribution to the Discussion of ‘Experimental evaluation of algorithm-assisted human decision-making: application to pretrial public safety assessment’ by Imai et al.
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A semi-supervised Bayesian mixture modelling approach for joint batch correction and classification
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Penalized Decomposition Using Residuals (PeDecURe) for Nuisance Variable Adjustment in Multivariate Pattern Analysis
inferred← citeshttps://doi.org/10.1109/icdmw51313.2020.00069openalex-snapshot
FairMixRep: Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints
inferred← citeshttps://doi.org/10.1145/3711896.3736895openalex-snapshot
Counterfactual Fairness Through Transforming Data Orthogonal to Bias
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Motion-invariant variational autoencoding of brain structural connectomes
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Constrained Reweighting of Distributions: An Optimal Transport Approach
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Fundamental Limits and Tradeoffs in Invariant Representation Learning
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