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.

168,694 papers · 148 categories

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5 results for “feature-targeted”

Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruningcs.LG

SFP prunes ID features to improve OOD generalization without domain data.

problem Improving out-of-distribution (OOD) generalization in biased models.

method Spurious Feature-targeted model Pruning (SFP) framework.

result SFP achieves optimal OOD generalization by pruning ID features.

model pruning out-of-distribution feature-targeted bias mitigation substructure exploration

2022-12-19abs ↗pdf ↗

Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecastingq-fin.RM

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting. Conventional ma…

2018-12-14abs ↗pdf ↗

A Guide to Feature Importance Methods for Scientific Inferencestat.ML

This paper provides a guide to feature importance methods for better scientific inference.

problem Limited understanding of data-generating process due to opaque ML model mechanisms.

method Comprehensive review and new proofs of global feature importance methods.

result Facilitates a thorough understanding and concrete recommendations for FI methods.

feature importance machine learning data-generating process interpretation scientific inference

2024-04-19abs ↗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 ↗

TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detectioncs.LG

TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.

problem Detecting axle-box bearing anomalies with only normal training data.

method Two-stage approach: pseudo anomalies, contrastive learning, KNN.

result Improves anomaly detection separability and sensitivity to degradation.

bearing time series anomaly detection pseudo anomalies contrastive learning k-nearest neighbors axle-box

2026-06-02abs ↗pdf ↗