23 papers
TimEE: End-to-end Time Series Classification via In-Context Learning
Jaris Küken, Shi Bin Hoo, Martin Mráz +2
Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- a…
LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
Hanning Yang, Meropi Karakioulaki, Lennart Purucker +3
Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of underlying mechanisms, which is par…
Beyond IID: How General Are Tabular Foundation Models, Really?
Lennart Purucker, Andrej Tschalzev, Nick Erickson +7
Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry. Research communities across disciplines are in…
SurvPFN: Towards Foundation Models for Survival Predictions
Samuel Böhm, Lennart Purucker, Frank Hutter +1
Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched. Un…
Towards Pretraining Text Encoders for TabPFN
Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1
Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text feat…
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,…