25 citations · 39 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest +4
Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especiall…
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,…
TabICLv2: A better, faster, scalable, and open tabular foundation model
Jingang Qu, David Holzmüller, Gaël Varoquaux +1
Tabular foundation models, such as TabPFNv2 and TabICL, have recently dethroned gradient-boosted trees at the top of predictive benchmarks, demonstrating the value of in-context le…
Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration
Alexandre Perez-Lebel, Gael Varoquaux, Sanmi Koyejo +2
Probabilistic classifiers are central for making informed decisions under uncertainty. Based on the maximum expected utility principle, optimal decision rules can be derived using…
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
Jingang Qu, David Holzmüller, Gaël Varoquaux +1
The long-standing dominance of gradient-boosted decision trees on tabular data is currently challenged by tabular foundation models using In-Context Learning (ICL): setting the tra…
Benchmarking missing-values approaches for predictive models on health databases
Alexandre Perez-Lebel, Gaël Varoquaux, Marine Le Morvan +2
BACKGROUND: As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values: incomplete observations. These large databa…
NeuMiss networks: differentiable programming for supervised learning with missing values
Marine Le Morvan, Julie Josse, Thomas Moreau +2
The presence of missing values makes supervised learning much more challenging. Indeed, previous work has shown that even when the response is a linear function of the complete dat…