Abhineet Agarwal
Abhineet Agarwal
Statistics PhD Candidate, University of California, Berkeley
Verified email at - Homepage
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Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.
A Agarwal*, YS Tan*, O Ronen, C Singh, B Yu
International Conference on Machine Learning, 111-135, 2022
Fast interpretable greedy-tree sums (FIGS)
YS Tan*, C Singh*, K Nasseri*, A Agarwal*, J Duncan, O Ronen, ...
arXiv preprint arXiv:2201.11931, 2022
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
YS Tan, A Agarwal, B Yu
International Conference on Artificial Intelligence and Statistics, 9663-9685, 2022
VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS
J Duncan*, R Kapoor*, A Agarwal*, C Singh*, B Yu
Journal of Open Source Software 7 (69), 3895, 2022
Synthetic combinations: A causal inference framework for combinatorial interventions
A Agarwal, A Agarwal, S Vijaykumar
Advances in Neural Information Processing Systems 36, 19195-19216, 2023
MDI+: A Flexible Random Forest-Based Feature Importance Framework
A Agarwal*, AM Kenney*, YS Tan*, TM Tang*, B Yu
arXiv preprint arXiv:2307.01932, 2023
Epistasis regulates genetic control of cardiac hypertrophy
Q Wang, T Tang, N Youlton, C Weldy, A Kenney, O Ronen, W Hughes, ..., 2023
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
L Sun, A Agarwal, A Kornblith, B Yu, C Xiong
arXiv preprint arXiv:2402.13448, 2024
Synthetic Combinations: A Causal Framework for Combinatorial Interventions
A Agarwal, A Agarwal, S Vijaykumar
arXiv preprint arXiv:2303.14226, 2024
An online dashboard for tracking, comparing, and forecasting COVID-19 in counties across the US
D Wang, M Shen, J Duncan, C Singh, T Tang, Y Wang, N Altieri, X Li, ...
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