1.9k citations · 7.2k across the 82 of their papers we have counts for
84 papers
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…
Univariate-Guided Sparse Regression
Sourav Chatterjee, Trevor Hastie, Robert Tibshirani
In this paper, we introduce ``UniLasso'' -- a novel statistical method for sparse regression. This two-stage approach preserves the signs of the univariate coefficients and leverag…
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…
Uncertainty Intervals for Prediction Errors in Time Series Forecasting
Hui Xu, Song Mei, Stephen Bates +2
Inference for prediction errors is critical in time series forecasting pipelines. However, providing statistically meaningful uncertainty intervals for prediction errors remains re…
Semi-supervised Cooperative Learning for Multiomics Data Fusion
Daisy Yi Ding, Xiaotao Shen, Michael Snyder +1
Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predi…