Boosting Certificate Robustness for Time Series Classification with Efficient Self-Ensemble Article Swipe
Chang George Dong
,
Zhixin Li
,
Liangwei Nathan Zheng
,
Weitong Chen
,
Wei Emma Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3627673.3679748
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3627673.3679748
Recently, the issue of adversarial robustness in the time series domain has garnered significant attention. However, the available defense mechanisms remain limited, with adversarial training being the predominant approach, though it does not provide theoretical guarantees. Randomized Smoothing has emerged as a standout method due to its ability to certify a provable lower bound on robustness radius under $\ell_p$-ball attacks. Recognizing its success, research in the time series domain has started focusing on these aspects. However, existing research predominantly focuses on time series forecasting, or under the non-$\ell_p$ robustness in statistic feature augmentation for time series classification~(TSC). Our review found that Randomized Smoothing performs modestly in TSC, struggling to provide effective assurances on datasets with poor robustness. Therefore, we propose a self-ensemble method to enhance the lower bound of the probability confidence of predicted labels by reducing the variance of classification margins, thereby certifying a larger radius. This approach also addresses the computational overhead issue of Deep Ensemble~(DE) while remaining competitive and, in some cases, outperforming it in terms of robustness. Both theoretical analysis and experimental results validate the effectiveness of our method, demonstrating superior performance in robustness testing compared to baseline approaches.
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- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3627673.3679748
- OA Status
- gold
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403577791
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https://openalex.org/W4403577791Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3627673.3679748Digital Object Identifier
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Boosting Certificate Robustness for Time Series Classification with Efficient Self-EnsembleWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-20Full publication date if available
- Authors
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Chang George Dong, Zhixin Li, Liangwei Nathan Zheng, Weitong Chen, Wei Emma ZhangList of authors in order
- Landing page
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https://doi.org/10.1145/3627673.3679748Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1145/3627673.3679748Direct OA link when available
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Boosting (machine learning), Computer science, Robustness (evolution), Artificial intelligence, Ensemble learning, Machine learning, Data mining, Pattern recognition (psychology), Biochemistry, Gene, ChemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.analysis | 173 |
| abstract_inverted_index.approach | 148 |
| abstract_inverted_index.aspects. | 74 |
| abstract_inverted_index.attacks. | 59 |
| abstract_inverted_index.baseline | 191 |
| abstract_inverted_index.compared | 189 |
| abstract_inverted_index.datasets | 113 |
| abstract_inverted_index.existing | 76 |
| abstract_inverted_index.focusing | 71 |
| abstract_inverted_index.garnered | 12 |
| abstract_inverted_index.limited, | 21 |
| abstract_inverted_index.margins, | 141 |
| abstract_inverted_index.modestly | 104 |
| abstract_inverted_index.overhead | 153 |
| abstract_inverted_index.performs | 103 |
| abstract_inverted_index.provable | 51 |
| abstract_inverted_index.reducing | 136 |
| abstract_inverted_index.research | 63, 77 |
| abstract_inverted_index.standout | 42 |
| abstract_inverted_index.success, | 62 |
| abstract_inverted_index.superior | 184 |
| abstract_inverted_index.training | 24 |
| abstract_inverted_index.validate | 177 |
| abstract_inverted_index.variance | 138 |
| abstract_inverted_index.Recently, | 0 |
| abstract_inverted_index.Smoothing | 37, 102 |
| abstract_inverted_index.addresses | 150 |
| abstract_inverted_index.approach, | 28 |
| abstract_inverted_index.available | 17 |
| abstract_inverted_index.effective | 110 |
| abstract_inverted_index.predicted | 133 |
| abstract_inverted_index.remaining | 159 |
| abstract_inverted_index.statistic | 90 |
| abstract_inverted_index.Randomized | 36, 101 |
| abstract_inverted_index.Therefore, | 117 |
| abstract_inverted_index.assurances | 111 |
| abstract_inverted_index.attention. | 14 |
| abstract_inverted_index.certifying | 143 |
| abstract_inverted_index.confidence | 131 |
| abstract_inverted_index.mechanisms | 19 |
| abstract_inverted_index.robustness | 5, 55, 88, 187 |
| abstract_inverted_index.struggling | 107 |
| abstract_inverted_index.Recognizing | 60 |
| abstract_inverted_index.adversarial | 4, 23 |
| abstract_inverted_index.approaches. | 192 |
| abstract_inverted_index.competitive | 160 |
| abstract_inverted_index.guarantees. | 35 |
| abstract_inverted_index.performance | 185 |
| abstract_inverted_index.predominant | 27 |
| abstract_inverted_index.probability | 130 |
| abstract_inverted_index.robustness. | 116, 170 |
| abstract_inverted_index.significant | 13 |
| abstract_inverted_index.theoretical | 34, 172 |
| abstract_inverted_index.augmentation | 92 |
| abstract_inverted_index.experimental | 175 |
| abstract_inverted_index.forecasting, | 83 |
| abstract_inverted_index.non-$\ell_p$ | 87 |
| abstract_inverted_index.$\ell_p$-ball | 58 |
| abstract_inverted_index.Ensemble~(DE) | 157 |
| abstract_inverted_index.computational | 152 |
| abstract_inverted_index.demonstrating | 183 |
| abstract_inverted_index.effectiveness | 179 |
| abstract_inverted_index.outperforming | 165 |
| abstract_inverted_index.predominantly | 78 |
| abstract_inverted_index.self-ensemble | 121 |
| abstract_inverted_index.classification | 140 |
| abstract_inverted_index.classification~(TSC). | 96 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.19872422 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |