2 citations · 6 across the 5 of their papers we have counts for
5 papers
ERICA: Quantifying Replicability of Cluster Analysis
Siamak K. Sorooshyari, Manuel A. Rivas, Robert Tibshirani
Despite being ubiquitous in science, clustering lacks a unified framework for quantitatively evaluating the replicability of its results. We present evaluating replicability via it…
Univariate-Guided Sparse Regression for Biobank-Scale High-Dimensional Omics Data
Joshua Richland, Tuomo Kiiskinen, William Wang +5
We present a scalable framework for computing polygenic risk scores (PRS) in high-dimensional genomic settings using the recently introduced Univariate-Guided Sparse Regression (un…
Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
Thomas Le Menestrel, Manuel Rivas
Docking is a crucial component in drug discovery aimed at predicting the binding conformation and affinity between small molecules and target proteins. ML-based docking has recentl…
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…