arXiv subjects

Lars Kleinemeier

Publications and source records attributed to Lars Kleinemeier.

3 recordsLinked to original sources

arXiv2026

Human Preference aligned Tabular Similarity

Task-agnostic tabular embeddings are increasingly used for similarity search in real-world business systems such as Product Lifecycle Management (PLM). However, leading embedding approaches are optimized primarily for prediction tasks - not for producing human preference aligned similarity rankings. We argue that standard downstream metrics are insufficient to fully assess embedding trustworthiness for similarity search and that human preference aligned evaluation is a necessary and currently missing component. We present a concrete evaluation procedure and illustrate the problem through a PLM use case.

cs.LG↗

arXiv2025

Comparing Task-Agnostic Embedding Models for Tabular Data

Recent foundation models for tabular data achieve strong task-specific performance via in-context learning. Nevertheless, they focus on direct prediction by encapsulating both representation learning and task-specific inference inside a single, resource-intensive network. This work specifically focuses on representation learning, i.e., on transferable, task-agnostic embeddings. We systematically evaluate task-agnostic representations extracted from tabular foundation models (TabPFN, TabICL and TabSTAR) alongside classical feature engineering (TableVectorizer and a sphere model) across a variety of application tasks as outlier detection (ADBench) and supervised learning (TabArena Lite). We find that simple feature engineering methods achieve comparable or superior performance while requiring significantly less computational resources than tabular foundation models.

cs.LG↗

arXiv2025

Generating Synthetic Relational Tabular Data via Structural Causal Models

Synthetic tabular data generation has received increasing attention in recent years, particularly with the emergence of foundation models for tabular data. The breakthrough success of TabPFN (Hollmann et al.,2025), which leverages vast quantities of synthetic tabular datasets derived from structural causal models (SCMs), demonstrates the critical role synthetic data plays in developing powerful tabular foundation models. However, most real-world tabular data exists in relational formats spanning multiple interconnected tables - a structure not adequately addressed by current generation methods. In this work, we extend the SCM-based approach by developing a novel framework that generates realistic synthetic relational tabular data including causal relationships across tables. Our experiments confirm that this framework is able to construct relational datasets with complex inter-table dependencies mimicking real-world scenarios.

cs.LG↗