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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.

168,695 papers · 148 categories

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9 results for “minipatches”

Fair MP-BOOST: Fair and Interpretable Minipatch Boostingstat.ML

Fair MP-Boost improves fairness and interpretability in boosting methods.

problem Improving fairness and interpretability in boosting methods.

method Fair MP-Boost uses adaptive sampling of minipatches to balance accuracy and fairness.

result Fair MP-Boost enhances fairness and accuracy while providing interpretable feature importance.

fairness interpretability boosting minipatches ensemble methods

2024-04-01abs ↗pdf ↗

MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Samplingstat.ML

MP-Boost boosts accuracy faster and more interpretable than AdaBoost.

problem Developing a faster, more interpretable boosting method.

method Adaptive selection of minipatches (small subsets of instances and features) at each iteration.

result Achieves comparable accuracy to AdaBoost and gradient boosting but faster and more interpretable.

boosting minipatch interpretable AdaBoost gradient boosting

2020-11-14abs ↗pdf ↗

Feature Selection for Huge Data via Minipatch Learningstat.ML

New method selects features for big data efficiently.

problem Feature selection challenges in huge data.

method Minipatch learning with STAMPS and AdaSTAMPS.

result AdaSTAMPS outperforms other methods in accuracy and speed.

feature selection huge data minipatch learning machine learning interpretability

2020-10-16abs ↗pdf ↗

Fast and Interpretable Consensus Clustering via Minipatch Learningstat.ML

IMPACC improves consensus clustering for bioinformatics data.

problem Consensus clustering's inefficiency and lack of interpretability for large-scale data.

method Ensemble minipatch co-occurrences, adaptive sampling of observations and features.

result Significantly improved accuracy and interpretability with substantial computational savings.

consensus clustering bioinformatics minipatch learning adaptive sampling cell types

2021-10-05abs ↗pdf ↗

Fast and Accurate Graph Learning for Huge Data via Minipatch Ensemblesstat.ML

Develops a fast method to learn graph structures from large datasets.

problem Learning graph structures from huge datasets with computational intractability and high complexity.

method Minipatch Graph (MPGraph) estimator: breaks up the problem into minipatches, uses hard thresholding, and integrates hyperparameter tuning.

result Proves graph selection consistency and empirically shows superior accuracy and speed compared to state-of-the-art methods.

graph learning huge data minipatch ensemble graphical models conditional dependence

2021-10-22abs ↗pdf ↗

LOCO Feature Importance Inference without Data Splitting via Minipatch Ensemblesstat.ML

A new method for feature importance inference without data splitting.

problem Feature importance inference for machine learning models.

method Minipatch ensembles for model-agnostic, distribution-free inference.

result Asymptotic validity of confidence intervals without data splitting.

feature importance machine learning interpretability minipatch ensembles distribution-free

2022-06-05abs ↗pdf ↗

Fair Feature Importance Scores via Feature Occlusion and Permutationcs.LG

Two new methods assess feature importance for fairness in machine learning models.

problem Understanding how features influence fairness in machine learning models.

method Two model-agnostic approaches: permutation and occlusion.

result Simple, scalable, and interpretable methods to quantify feature importance for fairness.

machine learning feature importance fairness permutation occlusion

2026-02-09abs ↗pdf ↗

Top-

k

Feature Importance Rankingcs.LG

RAMPART ranks top-k features more accurately than existing methods.

problem Accurate ranking of important features in machine learning.

method Adaptive sequential halving strategy combined with ensembling techniques.

result RAMPART achieves the correct top-k ranking with high probability.

feature importance ranking machine learning genomics halving strategy

2025-09-18abs ↗pdf ↗

Cluster LOCO: Feature Importance For Interpreting Clustersstat.ML

Cluster LOCO: A model-agnostic feature importance score for interpreting cluster outputs

problem Interpreting and auditing cluster outputs

method Cluster LOCO (Leave-One-Covariate-Out)

result More reliably recovers informative features than existing methods

cluster feature importance interpretation clustering feature occlusion

2026-06-12abs ↗pdf ↗