2 citations · 2 across the 2 of their papers we have counts for
3 papers
LaTable: Towards Large Tabular Models
Boris van Breugel, Jonathan Crabbé, Rob Davis +1
Tabular data is one of the most ubiquitous modalities, yet the literature on tabular generative foundation models is lagging far behind its text and vision counterparts. Creating s…
TRIAGE: Characterizing and auditing training data for improved regression
Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian +1
Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, c…
Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts
Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar +2
What distinguishes robust models from non-robust ones? While for ImageNet distribution shifts it has been shown that such differences in robustness can be traced back predominantly…