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Automated Machine Learning for Unsupervised Tabular Tasks
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim +2
In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsuperv…
How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset.…
Meta-Learning Transformers to Improve In-Context Generalization
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh +3
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms t…
Occam's model: Selecting simpler representations for better transferability estimation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Fine-tuning models that have been pre-trained on large datasets has become a cornerstone of modern machine learning workflows. With the widespread availability of online model repo…
Unlocking [CLS] Features for Continual Post-Training
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Joaquin Vanschoren
Continual learning requires models to integrate new classes or domains over time while preserving previously acquired knowledge. Within this paradigm, foundation models often achie…