4 papers
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks
Benjamin Feuer, Chinmay Hegde, Niv Cohen
Tabular classification has traditionally relied on supervised algorithms, which estimate the parameters of a prediction model using its training data. Recently, Prior-Data Fitted N…
Exploring Dataset-Scale Indicators of Data Quality
Benjamin Feuer, Chinmay Hegde
Modern computer vision foundation models are trained on massive amounts of data, incurring large economic and environmental costs. Recent research has suggested that improving data…
Distributionally Robust Classification on a Data Budget
Benjamin Feuer, Ameya Joshi, Minh Pham +1
Real world uses of deep learning require predictable model behavior under distribution shifts. Models such as CLIP show emergent natural distributional robustness comparable to hum…
LiT Tuned Models for Efficient Species Detection
Andre Nakkab, Benjamin Feuer, Chinmay Hegde
Recent advances in training vision-language models have demonstrated unprecedented robustness and transfer learning effectiveness; however, standard computer vision datasets are im…