(De-)Randomized Smoothing for Decision Stump Ensembles
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored, in contrast to those focusing on neural networks. Targeting this important challenge, we propose deterministic smoothing for decision stump ensembles. Whereas most prior work on randomized smoothing focuses on evaluating arbitrary base models approximately under input randomization, the key insight of our work is that decision stump ensembles enable exact yet effiient evaluation via dynamic programming. Importantly, we obtain deterministic robustness certificates, even jointly over numerical and categorical features, a setting ubiquitous in the real world. Further, we derive an MLE-optimal training method for smoothed decision stumps under randomization and propose two boosting approaches to improve their provable robustness. An extensive experimental evaluation on computer vision and tabular data tasks shows that our approach yields signficantly higher certified accuracies than the state-of-the-art for tree-based models. We release all code and trained models at https://github.com/eth-sri/drs.
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Publication status
published
Book title
Advances in Neural Information Processing Systems 35
Journal / series
Volume
Pages / Article No.
3066 - 3081
Publisher
Curran
Event
36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Adversarial Robustness; Certified Robustness; Randomized Smoothing
Organisational unit
03948 - Vechev, Martin / Vechev, Martin
Notes
Poster presentation on November 30, 2022.
Funding
Related publications and datasets
Is supplemented by: https://github.com/eth-sri/drs