9 citations · 36 across the 7 of their papers we have counts for
7 papers
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
Abhineet Agarwal, Ana M. Kenney, Yan Shuo Tan +2
Random forests (RFs) are among the most popular supervised learning algorithms due to their nonlinear flexibility and ease-of-use. However, as black box models, they can only be in…
Explaining black box text modules in natural language with language models
Chandan Singh, Aliyah R. Hsu, Richard Antonello +4
Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have cre…
Bridging Discrete and Backpropagation: Straight-Through and Beyond
Liyuan Liu, Chengyu Dong, Xiaodong Liu +2
Backpropagation, the cornerstone of deep learning, is limited to computing gradients for continuous variables. This limitation poses challenges for problems involving discrete late…
A Mixing Time Lower Bound for a Simplified Version of BART
Omer Ronen, Theo Saarinen, Yan Shuo Tan +2
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression algorithm. The posterior is a distribution over sums of decision trees, and predictions ar…
Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data
Keyan Nasseri, Chandan Singh, James Duncan +2
Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) mai…
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods
Abhineet Agarwal, Yan Shuo Tan, Omer Ronen +2
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice. To mitigate overfitting, trees are typically regularized by…
Fast Interpretable Greedy-Tree Sums
Yan Shuo Tan, Chandan Singh, Keyan Nasseri +6
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In…