More Like this

1. Deglacial temperature and carbonate saturation state variability in the tropical Atlantic at Antarctic Intermediate Water Depths

   https://doi.org/10.5281/zenodo.8229878  

   Oppo, DW; Lu, W; Huang, KF; Umling, NE; Guo, W; Yu, J; Curry, WB; Marchitto, TM; Wang, S (August 2023, Zenodo)

   Data and R script repository for the manuscript to reproduce figs 5 and 6; Deglacial temperature and carbonate saturation state variability in the tropical Atlantic at Antarctic Intermediate Water Depths" (Oppo et al., 2023) 

   more » « less
2. Supporting Information for The Future of Ground Magnetometer Arrays in Support of Space Weather Monitoring and Research

   Engebretson, Mark; Zesta, Eftyhia (July 2017, Space weather)

   This file contains three tables and one figure. Table S1 lists the institutions represented at the initial Ground Magnetometer Array Workshop. Table S2 contains information about all NSF-AGS supported and USGS ground-based magnetometer arrays as of Fall 2016 and Figure S1 is a map showing these arrays. Table S3 lists web sites serving ground magnetometer data. 

   more » « less
3. Fast Interpretable Greedy-Tree Sums (FIGS)

   Tan, Yan Shuo; Singh, Chandan; Nasseri, Keyan; Agarwal, Abhineet; Duncan, James; Ronen, Omer; Epland, Matthew; Kornblith, Aaron; Yu, Bin (July 2023, ArXivorg)

   Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the CART algorithm to simultaneously grow a flexible number of trees in summation. By combining logical rules with addition, FIGS is able to adapt to additive structure while remaining highly interpretable. Extensive experiments on real-world datasets show that FIGS achieves state-of-the-art prediction performance. To demonstrate the usefulness of FIGS in high-stakes domains, we adapt FIGS to learn clinical decision instruments (CDIs), which are tools for guiding clinical decision-making. Specifically, we introduce a variant of FIGS known as G-FIGS that accounts for the heterogeneity in medical data. G-FIGS derives CDIs that reflect domain knowledge and enjoy improved specificity (by up to 20% over CART) without sacrificing sensitivity or interpretability. To provide further insight into FIGS, we prove that FIGS learns components of additive models, a property we refer to as disentanglement. Further, we show (under oracle conditions) that unconstrained tree-sum models leverage disentanglement to generalize more efficiently than single decision tree models when fitted to additive regression functions. Finally, to avoid overfitting with an unconstrained number of splits, we develop Bagging-FIGS, an ensemble version of FIGS that borrows the variance reduction techniques of random forests. Bagging-FIGS enjoys competitive performance with random forests and XGBoost on real-world datasets. 

   more » « less
4. Fast Interpretable Greedy-Tree Sums

   https://doi.org/10.1073/pnas.2310151122  

   Tan, Yan Shuo; Singh, Chandan; Nasseri, Keyan; Agarwal, Abhineet; Duncan, James; Ronen, Omer; Epland, Matthew; Kornblith, Aaron; Yu, Bin (February 2025, Proceedings of the National Academy of Sciences)

   Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the Classification and Regression Trees (CART) algorithm to simultaneously grow a flexible number of trees in summation. By combining logical rules with addition, FIGS adapts to additive structure while remaining highly interpretable. Experiments on real-world datasets show FIGS achieves state-of-the-art prediction performance. To demonstrate the usefulness of FIGS in high-stakes domains, we adapt FIGS to learn clinical decision instruments (CDIs), which are tools for guiding decision-making. Specifically, we introduce a variant of FIGS known as Group Probability-Weighted Tree Sums (G-FIGS) that accounts for heterogeneity in medical data. G-FIGS derives CDIs that reflect domain knowledge and enjoy improved specificity (by up to 20% over CART) without sacrificing sensitivity or interpretability. Theoretically, we prove that FIGS learns components of additive models, a property we refer to as disentanglement. Further, we show (under oracle conditions) that tree-sum models leverage disentanglement to generalize more efficiently than single tree models when fitted to additive regression functions. Finally, to avoid overfitting with an unconstrained number of splits, we develop Bagging-FIGS, an ensemble version of FIGS that borrows the variance reduction techniques of random forests. Bagging-FIGS performs competitively with random forests and XGBoost on real-world datasets. 

   more » « less
5. Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data

   Nasseri, Keyan; Singh, Chandan; Duncan, James; Kornblith, Aaron; Yu, Bin (May 2022, ArXivorg)

   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) maintaining interpretability. To address these challenges, we propose an instance-weighted tree-sum method that effectively pools data across diverse groups to output a concise, rule-based model. Given distinct groups of instances in a dataset (e.g., medical patients grouped by age or treatment site), our method first estimates group membership probabilities for each instance. Then, it uses these estimates as instance weights in FIGS (Tan et al., 2022), to grow a set of decision trees whose values sum to the final prediction. We call this new method Group Probability-Weighted Tree Sums (G-FIGS). G-FIGS achieves state-of-theart prediction performance on important clinical datasets; e.g., holding the level of sensitivity fixed at 92%, G-FIGS increases specificity for identifying cervical spine injury (CSI) by up to 10% over CART and up to 3% over FIGS alone, with larger gains at higher sensitivity levels. By keeping the total number of rules below 16 in FIGS, the final models remain interpretable, and we find that their rules match medical domain expertise. All code, data, and models are released on Github. 

   more » « less