3 Search Results for "Wang, Yiyuan"

Document
DOI: 10.4230/LIPIcs.TIME.2024.17
Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN

Authors: Ibrahim Delibasoglu and Fredrik Heintz

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
Anomaly detection in time series data is a critical task in various domains, including finance, healthcare, cybersecurity and industry. Traditional methods, such as time series decomposition, clustering, and density estimation, have provided robust solutions for identifying anomalies that exhibit distinct patterns or significant deviations from normal data distributions. Recent advancements in machine learning and deep learning have further enhanced these capabilities. This paper introduces a novel method for anomaly detection that combines the strengths of autoencoders and recurrent neural networks (RNNs) with an reconstruction error feedback mechanism based on Mean Squared Error. We compare our method against classical techniques and recent approaches like OmniAnomaly, which leverages stochastic recurrent neural networks, and the Anomaly Transformer, which introduces association discrepancy to capture long-range dependencies and DCDetector using contrastive representation learning with multi-scale dual attention. Experimental results demonstrate that our method achieves superior overall performance in terms of precision, recall, and F1 score. The source code is available at http://github.com/mribrahim/AE-FAR
Cite as

Ibrahim Delibasoglu and Fredrik Heintz. Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 17:1-17:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{delibasoglu_et_al:LIPIcs.TIME.2024.17,
  author =	{Delibasoglu, Ibrahim and Heintz, Fredrik},
  title =	{{Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{17:1--17:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.17},
  URN =		{urn:nbn:de:0030-drops-212244},
  doi =		{10.4230/LIPIcs.TIME.2024.17},
  annote =	{Keywords: Time series, Anomaly, Neural networks}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.41
Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization

Authors: Wenbo Zhou, Yujiao Zhao, Yiyuan Wang, Shaowei Cai, Shimao Wang, Xinyu Wang, and Minghao Yin

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
Pseudo-Boolean optimization (PBO) is usually used to model combinatorial optimization problems, especially for some real-world applications. Despite its significant importance in both theory and applications, there are few works on using local search to solve PBO. This paper develops a novel local search framework for PBO, which has three main ideas. First, we design a two-level selection strategy to evaluate all candidate variables. Second, we propose a novel deep optimization strategy to disturb some search spaces. Third, a sampling flipping method is applied to help the algorithm jump out of local optimum. Experimental results show that the proposed algorithms outperform three state-of-the-art PBO algorithms on most instances.
Cite as

Wenbo Zhou, Yujiao Zhao, Yiyuan Wang, Shaowei Cai, Shimao Wang, Xinyu Wang, and Minghao Yin. Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 41:1-41:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{zhou_et_al:LIPIcs.CP.2023.41,
  author =	{Zhou, Wenbo and Zhao, Yujiao and Wang, Yiyuan and Cai, Shaowei and Wang, Shimao and Wang, Xinyu and Yin, Minghao},
  title =	{{Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{41:1--41:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.41},
  URN =		{urn:nbn:de:0030-drops-190784},
  doi =		{10.4230/LIPIcs.CP.2023.41},
  annote =	{Keywords: Local Search, Pseudo-Boolean Optimization, Deep Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.39
Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search

Authors: Bohan Li, Kai Wang, Yiyuan Wang, and Shaowei Cai

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
The minimum weighted connected dominating set (MWCDS) problem is an important variant of connected dominating set problems with wide applications, especially in heterogenous networks and gene regulatory networks. In the paper, we develop a nested local search algorithm called NestedLS for solving MWCDS on classic benchmarks and massive graphs. In this local search framework, we propose two novel ideas to make it effective by utilizing previous search information. First, we design the restart based smoothing mechanism as a diversification method to escape from local optimal. Second, we propose a novel inner-layer local search method to enlarge the candidate removal set, which can be modelled as an optimized version of spanning tree problem. Moreover, inner-layer local search method is a general method for maintaining the connectivity constraint when dealing with massive graphs. Experimental results show that NestedLS outperforms state-of-the-art meta-heuristic algorithms on most instances.
Cite as

Bohan Li, Kai Wang, Yiyuan Wang, and Shaowei Cai. Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 39:1-39:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{li_et_al:LIPIcs.CP.2021.39,
  author =	{Li, Bohan and Wang, Kai and Wang, Yiyuan and Cai, Shaowei},
  title =	{{Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{39:1--39:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.39},
  URN =		{urn:nbn:de:0030-drops-153304},
  doi =		{10.4230/LIPIcs.CP.2021.39},
  annote =	{Keywords: Operations Research, NP-hard Problem, Local Search, Weighted Connected Dominating Set Problem}
}
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