5 papers
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings
Félix Lefebvre, David Holzmüller, Gioia Blayer +3
Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical tables but require strings…
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,…
Scalable Feature Learning on Huge Knowledge Graphs for Downstream Machine Learning
Félix Lefebvre, Gaël Varoquaux
Many machine learning tasks can benefit from external knowledge. Large knowledge graphs store such knowledge, and embedding methods can be used to distill it into ready-to-use vect…
Table Foundation Models: on knowledge pre-training for tabular learning
Myung Jun Kim, Félix Lefebvre, Gaëtan Brison +2
Table foundation models bring high hopes to data science: pre-trained on tabular data to embark knowledge or priors, they should facilitate downstream tasks on tables. One specific…
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
Riccardo Cappuzzo, Aimee Coelho, Felix Lefebvre +2
Machine-learning from a disparate set of tables, a data lake, requires assembling features by merging and aggregating tables. Data discovery can extend autoML to data tables by aut…