Holistic deep learning
Name
10994_2023_Article_6482.pdf
Size
4.1 MB
Format
Adobe PDF
Checksum (MD5)
ef40686464547ddbc2395bfae2629ad4
Author(s)
Bertsimas, Dimitris
Villalobos Carballo, Kimberly
Boussioux, Léonard
Li, Michael L.
Paskov, Alex
Paskov, Ivan
Date Issued
December 7, 2023
Publisher
Springer US
Citation
Bertsimas, Dimitris, Villalobos Carballo, Kimberly, Boussioux, Léonard, Li, Michael L., Paskov, Alex et al. 2023. "Holistic deep learning."
Version
Final published version
Abstract
This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at
https://github.com/kimvc7/HDL
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-023-06482-y
