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.

168,742 papers · 148 categories

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4 results for “missing-indicators”

No imputation without representationcs.LG

Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.

problem Missing values in datasets can be lost through imputation, potentially reducing model performance.

method Combines missing-indicator approach with imputation strategies on real datasets.

result Missing-indicators generally increase classification performance, especially for categorical attributes.

imputation missing-indicators machine learning classification categorical attributes

2022-06-28abs ↗pdf ↗

The Missing Indicator Method: From Low to High Dimensionscs.LG

MIM adds indicator variables to improve model performance on incomplete data.

problem Missing data in incomplete data sets.

method Missing Indicator Method (MIM) and Selective MIM (SMIM).

result MIM improves model performance for informative missing values and high-dimensional data.

missing data imputation indicator variables model performance high-dimensional data

2022-11-16abs ↗pdf ↗

A Comparison of Modeling Preprocessing Techniquesstat.ME

This paper compares preprocessing techniques for XGBoost models on various data sets.

problem Improving predictive performance of XGBoost models through optimal data preprocessing.

method Comparison of feature selection, categorical handling, and null imputation methods.

result XGBoost importance by gain is the most consistent and highest-performing method for feature selection.

XGBoost preprocessing feature selection categorical encoding missing data

2023-02-23abs ↗pdf ↗

Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Datastat.ML

New method reduces bias in incomplete data using deliberate missingness.

problem Systematic gradient biases in incomplete data for stochastic learning.

method Richardson-SGD debiasing procedure with deliberate missingness.

result Reduces gradient bias from

O(\|p\|)

O(\|p\|^2)

missing data gradient bias Richardson extrapolation stochastic gradient descent imputation

2026-05-19abs ↗pdf ↗