2 citations · 5 across the 7 of their papers we have counts for
7 papers
Univariate-Guided Interaction Modeling
Aymen Echarghaoui, Robert Tibshirani
We propose a procedure for sparse regression with pairwise interactions, by generalizing the Univariate Guided Sparse Regression (UniLasso) methodology. A central contribution is o…
Pre-validation Revisited
Jing Shang, Sourav Chatterjee, Trevor Hastie +1
Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of the resu…
LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization
Erica Zhang, Ryunosuke Goto, Naomi Sagan +7
We introduce LLM-Lasso, a novel framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. Unlike traditional methods that rely…
powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities
François Grolleau, Robert Tibshirani, Jonathan H. Chen
Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often inv…
Adaptive Forward Stepwise Regression
Ivy Zhang, Robert Tibshirani
This paper proposes a sparse regression method that continuously interpolates between Forward Stepwise selection (FS) and the LASSO. When tuned appropriately, our solutions are muc…
Semiparametric conformal prediction
Ji Won Park, Robert Tibshirani, Kyunghyun Cho
Many risk-sensitive applications require well-calibrated prediction sets over multiple, potentially correlated target variables, for which the prediction algorithm may report corre…
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…