Summary of Boosting Certified Robustness For Time Series Classification with Efficient Self-ensemble, by Chang Dong et al.
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble
by Chang Dong, Zhengyang Li, Liangwei Zheng, Weitong Chen, Wei Emma Zhang
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed self-ensemble method enhances the lower bound of probability confidence for predicted labels by reducing variance of classification margins, certifying a larger radius, thereby addressing the limitations of Randomized Smoothing and Deep Ensemble. The approach is theoretically analyzed and experimentally validated, demonstrating superior performance in robustness testing compared to baseline approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method to make predictions more accurate and robust against attacks in time series data. It’s an improvement over existing methods like Randomized Smoothing, which can struggle with certain types of datasets. The new approach, called self-ensemble, does better than other methods like Deep Ensemble in some cases. The researchers tested their method and showed it works well. |
Keywords
» Artificial intelligence » Classification » Probability » Time series
