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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.

168,742 papers · 148 categories

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4 results for “HSIC-Lasso”

Post-selection inference with HSIC-Lassomath.ST

Proposes HSIC-Lasso for selective inference in non-linear data.

problem Detecting influential features in non-linear and high-dimensional data.

method Model-free HSIC-Lasso based on truncated Gaussians and polyhedral lemma.

result Tight control of type-I error even for small sample sizes.

HSIC-Lasso non-linear data variable selection post-selection inference selective inference

2020-10-29abs ↗pdf ↗

GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscs.LG

GraphLIME explains GNN models by selecting key features locally.

problem Explaining the effectiveness of GNN models is challenging due to complex nonlinear transformations.

method GraphLIME uses HSIC Lasso for nonlinear feature selection in GNN models.

result GraphLIME provides more descriptive explanations than existing methods.

Graph Neural Networks Local Interpretable Model Explanations Hilbert-Schmidt Independence Criterion Nonlinear Feature Selection Graph Explanations

2020-01-17abs ↗pdf ↗

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Datastat.ML

Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here we introduce the first feature selection method for nonlinear learning problems that can scale up t…

2016-08-14abs ↗pdf ↗

Sparse minimum Redundancy Maximum Relevance for feature selectionstat.ML

Proposes a new feature selection method integrating feature relationships.

problem Feature selection in machine learning models.

method Integrates feature-feature and feature-target relationships via penalized mRMR.

result Correctly identifies inactive features, reducing false discoveries.

feature selection machine learning feature screening mRMR knockoff filter

2025-08-26abs ↗pdf ↗