no code implementations • 21 Feb 2024 • Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu, Chenyan Xiong
Using publicly available patient data, we collaborate with ED clinicians to curate MIMIC-ED-Assist, a benchmark that measures the ability of AI systems in suggesting laboratory tests that minimize ED wait times, while correctly predicting critical outcomes such as death.
2 code implementations • 4 Jul 2023 • Abhineet Agarwal, Ana M. Kenney, Yan Shuo Tan, Tiffany M. Tang, Bin Yu
We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$.
1 code implementation • NeurIPS 2023 • Abhineet Agarwal, Anish Agarwal, Suhas Vijaykumar
Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i. e., $N \times 2^p$ causal parameters.
2 code implementations • 2 Feb 2022 • Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice.
2 code implementations • 28 Jan 2022 • Yan Shuo Tan, Chandan Singh, Keyan Nasseri, Abhineet Agarwal, James Duncan, Omer Ronen, Matthew Epland, Aaron Kornblith, Bin Yu
In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
1 code implementation • 18 Oct 2021 • Yan Shuo Tan, Abhineet Agarwal, Bin Yu
We prove a sharp squared error generalization lower bound for a large class of decision tree algorithms fitted to sparse additive models with $C^1$ component functions.