# Enhancing Certified Robustness via Smoothed Weighted Ensembling

arXiv: https://arxiv.org/abs/2005.09363 · HTML: https://mlsanity.com/paper/2005.09363/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 7 of 7)

- [Boosting Randomized Smoothing with Variance Reduced Classifiers](https://mlsanity.com/paper/2106.06946/) (2021)
- [On the Certified Robustness for Ensemble Models and Beyond](https://mlsanity.com/paper/2107.10873/) (2021)
- [Adversarial Training Helps Transfer Learning via Better Representations](https://mlsanity.com/paper/2106.10189/) (2021)
- [Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model](https://mlsanity.com/paper/2105.02803/) (2021)
- [Rethinking Randomized Smoothing from the Perspective of Scalability](https://mlsanity.com/paper/2312.12608/) (2023)
- [Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness](https://mlsanity.com/paper/2308.00346/) (2023)
- [Smooth-Reduce: Leveraging Patches for Improved Certified Robustness](https://mlsanity.com/paper/2205.06154/) (2022)

## Abstract (arXiv)

Randomized smoothing has achieved state-of-the-art certified robustness against l2-norm adversarial attacks. However, it is not wholly resolved on how to find the optimal base classifier for randomized smoothing. In this work, we employ a Smoothed WEighted ENsembling (SWEEN) scheme to improve the performance of randomized smoothed classifiers. We show the ensembling generality that SWEEN can help achieve optimal certified robustness. Furthermore, theoretical analysis proves that the optimal SWEEN model can be obtained from training under mild assumptions. We also develop an adaptive prediction algorithm to reduce the prediction and certification cost of SWEEN models. Extensive experiments show that SWEEN models outperform the upper envelope of their corresponding candidate models by a large margin. Moreover, SWEEN models constructed using a few small models can achieve comparable performance to a single large model with a notable reduction in training time.