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

Trend · papers per month

count % of corpus

17 results for “Feature-Augmentation”

Feature Augmentations for High-Dimensional Learningstat.AP

Enhances feature augmentation for high-dimensional learning.

problem Correlated high-dimensional measurements require dimensionality reduction.

method Augment features with factors extracted from design matrices and their transformations.

result Significantly weakens correlations between input variables, improving interpretability and numerical stability.

feature augmentation high-dimensional learning dimensionality reduction PCA kernel tricks

2025-08-29abs ↗pdf ↗

Robust Classification using Robust Feature Augmentationcs.LG

Existing deep neural networks, say for image classification, have been shown to be vulnerable to adversarial images that can cause a DNN misclassification, without any perceptible change to an image. In this work, we propose shock absorbing robust features such as binarization, e.g., rounding, and group extraction, e.g…

2019-05-26abs ↗pdf ↗

Adaptive Generation Model: A New Ensemble Methodcs.LG

AGM improves model accuracy through adaptive generation and feature augmentation.

problem Improving model accuracy in machine learning competitions.

method Adaptive Generation Model (AGM) using stacked ensemble learning with horizontal and vertical model expansion and feature augmentation.

result AGM outperforms previous models in 7 data sets.

machine learning ensemble learning stacking gcForest adaptive generation

2020-09-14abs ↗pdf ↗

Fairness for AUC via Feature Augmentationcs.LG

Improves AUC for disadvantaged groups by adding features.

problem Reducing cross-group differences in AUC for classification models.

method Feature augmentation to improve AUC for disadvantaged groups.

result Significantly improves AUC for disadvantaged groups.

AUC fairness classification feature augmentation bias mitigation

2021-11-24abs ↗pdf ↗

Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)hep-ph

FAT-GAN simulates electron-proton scattering without theoretical assumptions.

problem Efficiently training GANs to simulate complex particle distributions.

method Developed FAT-GAN using transformed and augmented features to improve GAN performance.

result FAT-GAN accurately reproduces electron momenta distributions in electron-proton scattering.

GAN Electron-Proton Scattering Feature-Augmentation Transformed Features Electron-Ion Collider

2020-01-29abs ↗pdf ↗

Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classificationstat.ME

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taki…

2013-12-31abs ↗pdf ↗

Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentationstat.ML

With recent progress in algorithms and the availability of massive amounts of computation power, application of machine learning techniques is becoming a hot topic in the oil and gas industry. One of the most promising aspects to apply machine learning to the upstream field is the rock facies classification in reservoi…

2018-08-29abs ↗pdf ↗

Linear Bandits with Partially Observable Featuresstat.ML

We tackle linear bandits with partially observable features, achieving sublinear regret.

problem Linear regret due to unobserved features in partially observable linear bandits.

method Feature augmentation with orthogonal basis vectors and a doubly robust estimator.

result Sublinear regret bound of

ilde{O}(\sqrt{(d + d_h)T})

linear bandits partially observable features regret minimization feature augmentation doubly robust estimator

2025-02-10abs ↗pdf ↗

Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereumcs.LG

Enhances Ponzi scheme detection on Ethereum using time-aware metapaths.

problem Lack of temporal information in heterogeneous transaction graphs.

method Time-aware Metapath Feature Augmentation (TMFAug) module.

result Significant performance improvements in Ponzi scheme detection.

Ponzi scheme Ethereum Temporal information Heterogeneous graph Graph-based detection

2022-10-30abs ↗pdf ↗

Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Networkcs.LG

HGNN learns augmented features for deep multi-task learning.

problem Feature learning for deep multi-task learning.

method Hierarchical Graph Neural Network (HGNN) with two levels of graph neural networks.

result Significant performance improvement in classification tasks.

Graph Neural Network Multi-Task Learning Feature Augmentation Hierarchical Learning Attention Mechanism

2020-02-12abs ↗pdf ↗

Sequential Regression Learning with Randomized Algorithmsstat.ML

Randomized SINDy learns dynamic data structures using probabilistic methods.

problem Learning time-dependent data structures in dynamic systems.

method Sequential machine learning with a probabilistic approach, incorporating feature augmentation and Tikhonov regularization.

result Demonstrated effectiveness in regression and binary classification using real-world data.

Sequential learning Randomized algorithms Dynamic data Functional analysis PAC learning

2025-07-04abs ↗pdf ↗

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Imagescs.CV

DISPR uses diffusion models to predict 3D cell shapes from 2D images.

problem Predicting 3D cell shapes from 2D microscopy images.

method Diffusion model trained to predict 3D shapes from 2D microscopy images as a prior.

result Adding DISPR predictions to minority cell classes improves classification accuracy.

Diffusion models Inverse problem Cell shape prediction 2D microscopy 3D reconstruction

2022-08-30abs ↗pdf ↗

Feature-Augmented Neural Networks for Patient Note De-identificationcs.CL

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objectiv…

2016-10-30abs ↗pdf ↗

Understanding and Improving Feature Learning for Out-of-Distribution Generalizationcs.LG

FeAT improves OOD generalization by learning richer features.

problem Improving feature learning for out-of-distribution (OOD) generalization.

method Feature Augmented Training (FeAT) iteratively augments and retains features from different subsets of training data.

result FeAT effectively learns richer features, boosting OOD performance.

Feature Learning Out-of-Distribution OOD Generalization Deep Networks Empirical Risk Minimization

2023-04-22abs ↗pdf ↗

Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemblecs.LG

Enhances robustness for time series classification using self-ensemble method.

problem Limited adversarial robustness in time series classification.

method Proposes a self-ensemble method to improve Randomized Smoothing's robustness certification.

result Demonstrates superior robustness compared to baseline approaches.

time series classification adversarial robustness Randomized Smoothing self-ensemble certified robustness

2024-09-04abs ↗pdf ↗

An introduction to domain adaptation and transfer learningcs.LG

In machine learning, if the training data is an unbiased sample of an underlying distribution, then the learned classification function will make accurate predictions for new samples. However, if the training data is not an unbiased sample, then there will be differences between how the training data is distributed and…

2018-12-31abs ↗pdf ↗

Macro-aware time series forecasting via hierarchical mixed-frequency attention modelsq-fin.ST

HANET combines LSTM and attention mechanisms for better financial forecasting.

problem Lack of distinct macroeconomic regimes in financial datasets.

method Hierarchical Cross-Attention mechanism integrating long-run macro contexts with high-frequency market dynamics.

result HANET outperforms neural forecasters, especially during turbulent periods.

deep learning financial forecasting attention mechanisms macroeconomic regimes mixed-frequency

2026-05-30abs ↗pdf ↗