On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,291 papers · 148 categories

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8 results for “African-American”

Algorithmic Bias in Recidivism Prediction: A Causal Perspectivestat.ME

COMPAS recidivism predictions show racial bias against African Americans, study finds.

problem Racial bias in recidivism prediction algorithms.

method Causal analysis using FACT, a fairness measure grounded in causal inference.

result COMPAS shows racial bias against African American defendants, robust to unmeasured confounding.

recidivism bias COMPAS causal inference algorithmic fairness

2019-11-24abs ↗pdf ↗

SAFS: A Deep Feature Selection Approach for Precision Medicinecs.LG

Proposes deep feature selection for HTN risk factors in African-Americans.

problem Identifying significant risk factors for heart damage in African-Americans with hypertension.

method Uses stacked auto-encoders for feature learning and representation in deep architecture.

result Deep learning approach leads to better results in identifying LVMI risk factors.

deep learning feature selection precision medicine hypertension African-American

2017-04-20abs ↗pdf ↗

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicinecs.LG

SUBIC detects patient subgroups for personalized treatment of HTN in African-Americans.

problem Developing tailored treatment schemes for different patient subgroups.

method Supervised Biclustering using convex optimization.

result Identifies and prioritizes risk factors for HTN in African-Americans.

precision medicine patient subgroups convex optimization risk factors hypertension

2017-09-26abs ↗pdf ↗

Multi-Differential Fairness Auditor for Black Box Classifierscs.LG

Paper presents mdfa to identify victims of discrimination in black box classifiers.

problem Identifying victims of discrimination in black box classifiers.

method Reduces discrimination measurement to matching distributions and sensitive attribute coincidence prediction.

result Identifies African-American individuals at high risk of violent recidivism.

Machine Learning Discrimination Black Box Classifiers Fairness Recidivism

2019-03-18abs ↗pdf ↗

A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancercs.LG

Deep learning and active learning improve survival analysis for prostate cancer treatment recommendations.

problem Building accurate survival models from high-dimensional, sparse, and complex clinical data.

method Deep active survival analysis framework using labeled and unlabeled instances, active learning with oracle.

result Our approach outperforms baseline models in prostate cancer treatment recommendation.

survival analysis deep learning active learning prostate cancer precision treatment

2018-04-10abs ↗pdf ↗

Naive Dictionary On Musical Corpora: From Knowledge Representation To Pattern Recognitioncs.IR

The paper proposes a novel approach to music analysis using text mining techniques.

problem Analyzing musical documents using traditional text mining methods.

method Developed a Naive Dictionary of 'muselets' (musical words) of uniform length.

result Demonstrated reasonable topic modeling and pattern recognition results with a simplified dictionary.

music analysis text mining topic modeling pattern recognition muselets

2018-11-29abs ↗pdf ↗

Empirical Likelihood-Based Fairness Auditing: Distribution-Free Certification and Flaggingstat.ML

EL framework certifies and flags bias in ML models without distributional assumptions.

problem Systematic performance disparities across sensitive subpopulations in ML models.

method Empirical likelihood-based approach for non-parametric fairness auditing.

result EL framework outperforms bootstrap methods in certification and subpopulation discovery.

fairness auditing empirical likelihood machine learning bias detection non-parametric statistics

2026-01-28abs ↗pdf ↗

Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisationcs.LG

Proposes a new method for fairness in machine learning with multiple protected attributes.

problem Ensuring fairness in machine learning models with continuous and multiple protected attributes.

method Distance covariance regularisation framework to mitigate association between model predictions and protected attributes.

result Demonstrates effectiveness in mitigating fairness gerrymandering in regression tasks.

fairness machine learning protected attributes demographic parity distance covariance

2025-09-09abs ↗pdf ↗