6 papers
Distillation of Tabular Foundation Models into Efficient Predictors
Minho Jeong, Dooho Lee, Jinmo Lee +1
Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes inference expensive. Knowledge…
NodeGround: A Node Classification Benchmark in the Graph Foundation Model Era
Jinmo Lee, Dooho Lee, Minho Jeong +1
Can a pretrained graph model replace training and tuning a separate predictor for each dataset? Answering this requires evaluating prediction quality alongside computational cost.…
Molecular Property Prediction under Structural Shift with Tabular Foundation Models
Jinmo Lee, Dooho Lee, Minho Jeong +1
Predicting molecular properties for compounds that differ structurally from labeled training molecules is important for drug discovery and materials design. Tabular foundation mode…
Message Passing Does More with Less for In-Context Learning on Graphs
Dooho Lee, Jinmo Lee, Minho Jeong +2
Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeated costs and effort. Graph in…
Causilo Technical Report
Minyong Cho, Minho Jeong, Dooho Lee +2
We introduce Causilo, a tabular foundation model (TFM) that combines frontier predictive performance with exceptionally fast inference. On TabArena, Causilo achieves 1785.4 Elo, at…
View Space: Learning Representation across Arbitrary Graphs
Dooho Lee, Myeong Kong, Minho Jeong +1
Generalizing pretrained models to unseen datasets without retraining is a central challenge toward foundation models. Achieving fully inductive inference on numerical data is parti…