Trending:

AutoG: Towards automatic graph construction from tabular data

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM

The introduction of AutoG marks a pivotal advancement in the field of graph machine learning (GML), which has seen rapid growth yet often neglects the foundational step of graph construction from tabular data. This study identifies critical challenges, including the absence of dedicated datasets for evaluating graph construction methods and the reliance on human expertise in existing approaches. By proposing AutoG, the researchers aim to automate this process, significantly improving efficiency and quality. Experimental results indicate that AutoG can produce graphs that rival those crafted by human experts, underscoring the importance of graph quality for downstream task performance. This development not only fills a crucial gap in GML but also enhances the potential applications of graph-based models across various domains.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Recommended Readings

arXiv — cs.CLa day ago

Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis

NeutralArtificial Intelligence

Natural language interfaces for tabular data must address ambiguities in user queries. This paper reframes ambiguity as a feature of cooperative interaction, proposing a framework that shares responsibility for query specification between users and systems. It distinguishes between cooperative queries, which can be resolved through inference, and uncooperative queries that cannot. The study evaluates queries across 15 datasets, revealing a problematic mix of query types that complicates assessments of system accuracy and interpretation capabilities.

Read full article

via arXiv — cs.CL

arXiv — cs.LG2 days ago

Fair In-Context Learning via Latent Concept Variables

PositiveArtificial Intelligence

The paper titled 'Fair In-Context Learning via Latent Concept Variables' explores the in-context learning (ICL) capabilities of large language models (LLMs) in handling tabular data. It highlights the potential for LLMs to inherit biases from pre-training data, which can lead to discrimination in high-stakes applications. The authors propose an optimal demonstration selection method using latent concept variables to enhance task adaptation and fairness, alongside data augmentation strategies to minimize correlations between sensitive variables and predictive outcomes.

Read full article

via arXiv — cs.LG