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.

169,051 papers · 148 categories

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4 results for “geocoding”

Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplementsstat.ML

Improved surname geocoding and name supplements enhance race imputation accuracy.

problem Census data problems affecting race imputation accuracy.

method Fully Bayesian Improved Surname Geocoding (fBISG) and name supplements.

result Significant improvement in race imputation accuracy across all racial groups.

race imputation Bayesian inference surname geocoding measurement error voter files

2022-05-12abs ↗pdf ↗

Can We Trust Race Prediction?cs.LG

Improved race prediction model outperforms existing methods.

problem Improving race prediction using voter registration data.

method Trained BiLSTM model on voter registration data and created an ensemble.

result Achieved up to 36.8% higher OOS F1 scores than previous models.

race prediction voter registration BiLSTM F1 score ensemble

2023-07-17abs ↗pdf ↗

Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learningcs.CV

Deep learning generates addresses for under-addressed cities.

problem Lack of street addresses in developing countries.

method Deep learning applied to satellite images to generate addresses.

result Algorithm generates addresses for neighborhoods and regions.

deep learning street addresses developing countries satellite imagery geocoding

2018-11-10abs ↗pdf ↗

Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Featurescs.LG

Develops methods to measure and reduce fairness in datasets with limited protected attribute labels.

problem Measuring and reducing fairness in datasets with limited protected attribute labels.

method Proposes methods to estimate fairness metrics and train models to limit fairness violations using probabilistic protected attribute labels.

result Our methods provide tighter bounds on true disparity and effectively reduce fairness violations with lesser fairness-accuracy trade-offs.

fairness metrics probabilistic protected attributes conventional fairness fair optimization non-convex optimization

2023-10-02abs ↗pdf ↗