2 citations · 5 across the 5 of their papers we have counts for
5 papers
Interpretable AI with Local Distillation
Erin Craig, Yiling Huang, Snigdha Panigrahi
Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reasoning about their predictions.…
Supervised learning pays attention
Erin Craig, Robert Tibshirani
In-context learning with attention enables large neural networks to make context-specific predictions by selectively focusing on relevant examples. Here, we adapt this idea to supe…
MMIL: A novel algorithm for disease associated cell type discovery
Erin Craig, Timothy Keyes, Jolanda Sarno +5
Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introduce Mixture Modeling for Multiple…
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Thomas Le Menestrel, Erin Craig, Robert Tibshirani +2
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring…
Pretraining and the Lasso
Erin Craig, Mert Pilanci, Thomas Le Menestrel +7
Pretraining is a popular and powerful paradigm in machine learning to pass information from one model to another. As an example, suppose one has a modest-sized dataset of images of…