2 citations · 3 across the 12 of their papers we have counts for
4 papers · 1 filter
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In…
STRABLE: Benchmarking Tabular Machine Learning with Strings
Gioia Blayer, Myung Jun Kim, Félix Lefebvre +8
Benchmarking tabular learning has revealed the benefit of dedicated architectures, pushing the state of the art. But real-world tables often contain string entries, beyond numbers,…
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Alan Arazi, Eilam Shapira, Shoham Grunblat +8
Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numeric…
TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
Alan Arazi, Eilam Shapira, Roi Reichart
While deep learning has achieved remarkable success across many domains, it has historically underperformed on tabular learning tasks, which remain dominated by gradient boosting d…