36 citations · 128 across the 27 of their papers we have counts for
4 papers · 1 filter
TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
Rasa Hosseinzadeh, Alex Labach, Zexin Xue +3
Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity. However, recent approaches with either cell-based architectures or retrieval…
Understanding the Surprising Generalization Properties of Tabular Foundation Models
Nour Shaheen, Junwei Ma, Alex Labach +3
Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts labels for new inputs without upd…
Training Fair Tabular Foundation Models
Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini +2
Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.…
Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
Dae Yon Hwang, Raunaq Suri, Valentin Villecroze +4
LLM agents operate in two distinct regimes: open-weight agents amenable to reinforcement learning (RL) and black-box agents whose behaviour must be controlled purely at test time.…