From the 2 of 27 linked papers with an AI index.
27 papers
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.…
LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes
Michael Solodko, Steven Gong, Guangwei Yu +3
LakeQuest is a human‑validated benchmark of 9,846 question‑answer pairs for evaluating end‑to‑end retrieval and synthesis over heterogeneous data lakes across AI/ML metadata, retai…
Causal Foundation Models with Continuous Treatments
Christopher Stith, Medha Barath, Vahid Balazadeh +2
The paper introduces a causal foundation model that can predict individual treatment-response curves for continuous interventions, using a transformer trained on a synthetic causal…
TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell +2
Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak through model predictions. We fi…
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
Wei Cui, Tongzi Wu, Jesse C. Cresswell +2
Meta-learning represents a strong class of approaches for solving few-shot learning tasks. Nonetheless, recent research suggests that simply pre-training a generic encoder can pote…
A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization
Prasanth YSS, Zhichen Ren, Rasa Hosseinzadeh +6
Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but GRPO-style optimization remains prone to collapse. We analyse this instability through…