Search Results for author:
Found 7 papers, 2 papers with code
It’s quality and quantity: the effect of the amount of comments on online suicidal posts
We use propensity score stratification, a causal inference method for observational data, and estimate whether the amount of comments —as a measure of social support— increases or decreases the likelihood of posting again on SW. One hypothesis is that receiving more comments may decrease the likelihood of the user posting in SW in the future, either by reducing symptoms or because comments from untrained peers may be harmful.
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment.
Approximate Post-Selective Inference for Regression with the Group LASSO
After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected parameters is unreliable in the absence of adjustments for selection bias.
Supervised PCA: A Multiobjective Approach
Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more useful for a prediction task of interest.
Graph-aware Modeling of Brain Connectivity Networks
One challenge in analyzing such data is that inference at the individual edge level is not particularly biologically meaningful; interpretation is more useful at the level of so-called functional regions, or groups of nodes and connections between them; this is often called "graph-aware" inference in the neuroimaging literature.
Network classification with applications to brain connectomics
Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes.
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine
Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection.
