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Son T. Mai

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3 papers

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AAAI Conference 2025 Conference Paper

InteDisUX: Intepretation-Guided Discriminative User-Centric Explanation for Time Series

Viet-Hung Tran
Zichi Zhang
Tuan Dung Pham
Ngoc Phu Doan
Anh-Tuan Hoang
Peixin Li
Hans Vandierendonck
Ira Assent

Explanation for deep learning models on time series classification (TSC) tasks is an important and challenging problem. Most existing approaches use attribution maps to explain outcomes. However, they have limitations in generating explanations that are well-aligned with humans's perceptions. Recently LIME-based approaches provide a more meaningful explanation via segmenting the data. However, these approaches are still suffering from the processes of segment generations and evaluations. In this paper, we propose a novel time series explanation approach called InteDisUX to overcome these problems. Our technique utilizes the segment-level integrated gradient (SIG) for calculating importance scores for an initial set of small and equal segments before iteratively merge two consecutive ones to create better explanations under a unique greedy strategy guided by two new proposed metrics including discrimination and faithfulness gains. By this way, our method does not depend on predefined segments like others while being robusts to instability, poor local fidelity and data imbalance like LIME-based methods. Furthermore, InteDisUX is the first work to use the model's information to improve the set of segments} for time series explanation. Extensive experiments show that our method outperforms LIME-based ones in 12 datasets in terms of faithfulness and 8/12 datasets in terms of robustness.

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TMLR Journal 2025 Journal Article

Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?

Viet-Hung Tran
Ngoc-Bao Nguyen
Son T. Mai
Hans Vandierendonck
Ira Assent
Alex Kot
Ngai-Man Cheung

Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate on model-centric approaches, the impact of data on MI robustness remains largely unexplored. In this work, we explore Random Erasing (RE)—a technique traditionally used for improving model generalization under occlusion—and uncover its surprising effectiveness as a defense against MI attacks. Specifically, our novel feature space analysis shows that model trained with RE-images introduces a significant discrepancy between the features of MI-reconstructed images and those of the private data. At the same time, features of private images remain distinct from other classes and well-separated from different classification regions. These effects collectively degrade MI reconstruction quality and attack accuracy while maintaining reasonable natural accuracy. Furthermore, we explore two critical properties of RE including Partial Erasure and Random Location. First, Partial Erasure prevents the model from observing entire objects during training, and we find that this has significant impact on MI, which aims to reconstruct the entire objects. Second, the Random Location of erasure plays a crucial role in achieving a strong privacy-utility trade-off. Our findings highlight RE as a simple yet effective defense mechanism that can be easily integrated with existing privacy-preserving techniques. Extensive experiments of 37 setups demonstrate that our method achieves SOTA performance in privacy-utility tradeoff. The results consistently demonstrate the superiority of our defense over existing defenses across different MI attacks, network architectures, and attack configurations. For the first time, we achieve significant degrade in attack accuracy without decrease in utility for some configurations. Our code and additional results are available at: https://ngoc-nguyen-0.github.io/MIDRE/

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AAAI Conference 2025 Conference Paper

WaveletMixer: A Multi-Resolution Wavelets Based MLP-Mixer for Multivariate Long-Term Time Series Forecasting

Zichi Zhang
Tuan Dung Pham
Yimeng An
Ngoc Phu Doan
Majed Alsharari
Viet-Hung Tran
Anh-Tuan Hoang
Hans Vandierendonck

Time Series Forecasting (TSF) aims at predicting future values for a time series data and plays a crucial role in many real-world applications, e.g., finance, disease spread, or weather predictions. However, it is also a very challenging task due to complex temporal dependencies in the data, especially for long-term forecasting. In this paper, we introduce WaveletMixer, an iterative multi-levels, multi-resolutions and multi-phases approach to effectively capture long-term dependencies of multivariate time series in both global and local perspectives for improving forecasting performance. WaveletMixer fundamentally differs from existing works in the following key aspects. First, it exploits multi-levels properties of Wavelet transformation to create multiple forecasting models for different frequency domains at various levels of resolutions. Second, the relationships among different frequency domains are exploited to iteratively adjust all prediction models at all levels simultaneously in both local and global perspectives to reduce prediction errors and biases, thus significantly improving the final accuracy. Third, while WaveletMixer is a general framework that can be used to boost the performance of any deep-learning architecture (e.g., MLP, LSTM or Transformer), we additionally introduce TS-Learner, an MLP-based model to further enhance the performance in long-term forecasting. Extensive experiments have been conducted on nine real-world datasets to demonstrate the outstanding performance of WaveletMixer compared to SOTA methods and to reveal its important characteristics.

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