2 papers
cs.AI2026
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…
cs.LG2026
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…