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Xiawei Guo

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4 papers
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4

NeurIPS Conference 2023 Conference Paper

Combating Bilateral Edge Noise for Robust Link Prediction

  • Zhanke Zhou
  • Jiangchao Yao
  • Jiaxu Liu
  • Xiawei Guo
  • Quanming Yao
  • Li He
  • Liang Wang
  • Bo Zheng

Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated. To close this gap, we first conduct an empirical study to disclose that the edge noise bilaterally perturbs both input topology and target label, yielding severe performance degradation and representation collapse. To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse. Different from the basic information bottleneck, RGIB further decouples and balances the mutual dependence among graph topology, target labels, and representation, building new learning objectives for robust representation against the bilateral noise. Two instantiations, RGIB-SSL and RGIB-REP, are explored to leverage the merits of different methodologies, i. e. , self-supervised learning and data reparameterization, for implicit and explicit data denoising, respectively. Extensive experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations. The code is publicly available at: https: //github. com/tmlr-group/RGIB.

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ICML Conference 2023 Conference Paper

Exploring Model Dynamics for Accumulative Poisoning Discovery

  • Jianing Zhu
  • Xiawei Guo
  • Jiangchao Yao
  • Chao Du
  • Li He
  • Shuo Yuan
  • Tongliang Liu
  • Liang Wang 0001

Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples based on their distinct dynamics from the clean samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks. Extensive experiments comprehensively characterized Memorization Discrepancy and verified its effectiveness. The code is publicly available at: https: //github. com/tmlr-group/Memorization-Discrepancy.

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IJCAI Conference 2019 Conference Paper

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction

  • Quanming Yao
  • Xiawei Guo
  • James Kwok
  • Weiwei Tu
  • Yuqiang Chen
  • Wenyuan Dai
  • Qiang Yang

To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserving logistic regression by stacking. We show that this can be done either by sample-based or feature-based partitioning. However, we prove that when privacy-budgets are the same, feature-based partitioning requires fewer samples than sample-based one, and thus likely has better empirical performance. As transfer learning is difficult to be integrated with a differential privacy guarantee, we further combine the proposed method with hypothesis transfer learning to address the problem of learning across different organizations. Finally, we not only demonstrate the effectiveness of our method on two benchmark data sets, i. e. , MNIST and NEWS20, but also apply it into a real application of cross-organizational diabetes prediction from RUIJIN data set, where privacy is of a significant concern.

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AAAI Conference 2017 Conference Paper

Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm

  • Xiawei Guo
  • Quanming Yao
  • James Kwok

Most tensor problems are NP-hard, and low-rank tensor completion is much more difficult than low-rank matrix completion. In this paper, we propose a time and spaceefficient low-rank tensor completion algorithm by using the scaled latent nuclear norm for regularization and the Frank- Wolfe (FW) algorithm for optimization. We show that all the steps can be performed efficiently. In particular, FW’s linear subproblem has a closed-form solution which can be obtained from rank-one SVD. By utilizing sparsity of the observed tensor, we only need to maintain sparse tensors and a set of small basis matrices. Experimental results show that the proposed algorithm is more accurate, much faster and more scalable than the state-of-the-art.

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