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.

169,051 papers · 148 categories

Trend · papers per month

count % of corpus

9 results for “Attribute-aware”

Attribute-aware Collaborative Filtering: Survey and Classificationcs.IR

This paper surveys and classifies attribute-aware CF models.

problem Rating prediction with user and item attributes.

method Mathematical classification of attribute-aware CF models into four categories.

result Comprehensive comparison of effectiveness among different categories.

Collaborative Filtering Attribute-aware Rating Prediction User Attributes Item Attributes

2018-10-20abs ↗pdf ↗

Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervisioncs.IR

Hotel2vec learns hotel embeddings from multiple data sources.

problem Cold-start problem for hotels with insufficient click data.

method Self-supervised neural network architecture combining user clicks, hotel attributes, and geographic info.

result Improved downstream task predictions with structured hotel attributes.

Hotel Embeddings Neural Networks Self-Supervision Cold-Start Problem Attribute-Aware

2019-09-30abs ↗pdf ↗

Scalable attribute-aware network embedding with localitycs.LG

Adding attributes for nodes to network embedding helps to improve the ability of the learned joint representation to depict features from topology and attributes simultaneously. Recent research on the joint embedding has exhibited a promising performance on a variety of tasks by jointly embedding the two spaces. Howeve…

2018-04-17abs ↗pdf ↗

Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learningstat.ML

New research shows fairness in machine learning can sometimes make disadvantaged groups worse off.

problem The impact of fairness constraints in machine learning on different groups.

method Unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions.

result Fairness in machine learning can lead to leveling down, making one or both groups worse off.

fairness machine learning group fairness leveling down binary classification

2026-03-06abs ↗pdf ↗

Fair and Optimal Classification via Post-Processingcs.LG

Paper characterizes fairness vs. accuracy tradeoff in classification.

problem Mitigating bias in machine learning models.

method Characterizes the tradeoff between fairness and accuracy, provides a post-processing algorithm.

result Post-processing algorithm yields optimal fair classifier when score is Bayes optimal.

fairness classification post-processing Wasserstein-barycenter accuracy

2022-11-03abs ↗pdf ↗

Fair Classification by Direct Intervention on Operating Characteristicsstat.ML

New classifiers ensure fairness by adjusting a base classifier's operating characteristics.

problem Ensuring fairness in binary classification with multiple group constraints.

method Intervening directly on a base classifier's operating characteristics using group-wise ROC convex hulls and post-processing.

result Methods satisfy multiple fairness constraints (DP, EO, PP) with minimal interventions and near-oracle accuracy.

fairness classification operating characteristics ROC post-processing

2025-09-29abs ↗pdf ↗

Bayes-Optimal Fair Classification with Multiple Sensitive Featuresstat.ML

The paper tackles fair classification with multiple sensitive features.

problem Existing fair classification methods often consider a single sensitive feature, but in practice, individuals are defined by multiple sensitive features.

method Characterizes Bayes-optimal fair classifiers for multiple sensitive features under various fairness measures, proposing in-processing and post-processing algorithms.

result Bayes-optimal fair classifiers for multiple sensitive features are instance-dependent thresholding rules that rely on a weighted sum of group membership probabilities.

fair classification multiple sensitive features Bayes-optimal approximate fairness measures group fairness

2025-05-01abs ↗pdf ↗

Zero-Shot Image Classification Using Coupled Dictionary Embeddingcs.CV

A new ZSL algorithm uses shared sparse representations for unseen classes.

problem Classifying images from unseen classes using only semantic information.

method Coupled dictionary learning to represent visual and semantic features in an intermediate space.

result The proposed method outperforms state-of-the-art ZSL algorithms on benchmark datasets.

Zero-shot learning Coupled dictionary learning Semantic attributes Shared sparse representation Domain shift

2019-06-10abs ↗pdf ↗

From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Marketscs.AI

Benchmark evaluates LLM trading agents by masking identifiers to prevent memory leaks.

problem Evaluate LLM trading agents without relying on market memory or noise.

method Data-side masking protocol, Barra-style performance attribution framework.

result LLM agents' returns are largely explained by market and style exposure, not stock selection.

LLM trading benchmark memory masking performance attribution stock selection

2026-05-27abs ↗pdf ↗