5 papers
Enhancing Tabular Learners with Context-Aware Semantic Embeddings
Günther Schindler, Günther Schindler, Maximilian Schambach +2
While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the ric…
FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks
Marek Polewczyk, Maximilian Schambach, Marco Spinaci +2
We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a suite of task-specific decod…
Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
Myung Jun Kim, Maximilian Schambach, Frank Essenberger +2
Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks. Yet, little is known for enterprise data, w…
TabGemma: Text-Based Tabular ICL via LLM using Continued Pretraining and Retrieval
Günther Schindler, Maximilian Schambach, Michael Medek +1
We study LLMs for tabular prediction with mixed text, numeric, and categorical fields. We introduce TabGemma, a schema-agnostic in-context learner that treats rows as sequences and…
ConTextTab: A Semantics-Aware Tabular In-Context Learner
Marco Spinaci, Marek Polewczyk, Maximilian Schambach +1
Tabular in-context learning (ICL) has recently achieved state-of-the-art (SOTA) performance on several tabular prediction tasks. Previously restricted to classification problems on…