2 citations · 2 across the 9 of their papers we have counts for
7 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…
Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games
Eilam Shapira, Omer Madmon, Roi Reichart +1
Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficultie…
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…
Fairness under Competition
Ronen Gradwohl, Eilam Shapira, Moshe Tennenholtz
Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal…