Hung Nguyen, Pei-Yuan Wu, J. Morris Chang, “Federated Learning for distribution skewed data using sample weights,” IEEE Transactions on Artificial Intelligence, Dec. 2023
Wei-Jyun Hong, Chia-Yu Shen, Pei-Yuan Wu, “Multi-source wafer map retrieval based on contrastive learning for root cause analysis in semiconductor manufacturing,” Journal of Intelligent Manufacturing, Nov. 2023
Po-Hsuan Huang, Chia-Heng Tu, Shen-Ming Chung, Pei-Yuan Wu, Tung-Lin Tsai, Yi-An Lin, Chun-Yi Dai, Tzu-Yi Liao, “SecureTVM: A TVM-Based Compiler Framework for Selective Privacy-Preserving Neural Inference,” ACM Transactions on Design Automation of Electronic Systems, 28, 1-28, May 2023
Tsung-Hsien Lin, Ying-Shuo Lee, Fu-Chieh Chang, J. Morris Chang, Pei-Yuan Wu, “Protecting Sensitive Attributes by Adversarial Training through Class-Overlapping Techniques,” IEEE Transactions on Information Forensics and Security, 18, 1283-1294, Jan. 2023
Hao-Wei Chan, Pei-Yuan Wu, Alexander I-Chi Lai, Ruey-Beei Wu, “Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM,” IEEE Access, 10, 96260-96272, Sept. 2022
Chung-Yuan Chen, Alexander I-Chi Lai, Pei-Yuan Wu, Ruey-Beei Wu, “Optimization and Evaluation of Multi-Detector Deep Neural Network for High Accuracy Wi-Fi Fingerprint Positioning,” IEEE Internet of Things, 9(16), 15204-15214, Jan. 2022
Poh Yuen Chan, Alexander I-Chi Lai, Pei-Yuan Wu, Ruey-Beei Wu, “Physical Tampering Detection Using Single COTS Wi-Fi Endpoint,” Sensors, 21(16), 5665, Aug. 2021
Hung Nguyen, Di Zhuang, Pei-Yuan Wu, Morris Chang, “AutoGAN-based dimension reduction for privacy preservation,” Neurocomputing, vol. 384, 94-103, Apr. 2020
B. Tseng and P. Wu , “Compressive Privacy Generative Adversarial Network,” IEEE Transactions on Information Forensics and Security, vol. 15, 2499-2513, Jan. 2020
S. Y. Kung, Thee Chanyaswad, J. Morris Chang, and P. Y. Wu, “Collaborative PCA/DCA Learning Methods for Compressive Privacy,” ACM Transactions on Embedded Computing Systems, Volume 16 Issue 3, 76:1-76:18, Jul. 2017
P. Y. Wu, C. C. Fang, J. M. Chang, and S. Y. Kung, “Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System,” IEEE Trans. Cybern., vol. 47, no. 11, 3916-3927, Aug. 2016
J. M. Chang, C. C. Fang, K. H. Ho, N. Kelly, P. Y. Wu, Y. Ding, C. Chu, S. Gilbert, A. E. Kamal, S. Y. Kung, “Capturing Cognitive Fingerprints from Keystroke Dynamics,” IT Professional, vol. 15, 24-28, Jul. 2013
Conference & proceeding papers:
Shao-Yu Yen, Yen Ru Lai, Fu-Chieh Chang, Pei-Yuan Wu, “Thermal and Energy Management with Fan Control Through Offline Meta-Reinforcement Learning,” NeurIPS 2024 Workshop OWA, Vancouver, Canada, Dec. 2024
Yu-Ting Huang, Pei-Yuan Wu, Chuan-Ju Wang, “ECO: Efficient Computational Optimization for Exact Machine Unlearning in Deep Neural Networks,” WANT@ICML 2024, Vienna, Austria, Jul. 2024
Tung-Lin Tsai, Pei-Yuan Wu, “SepMM : A General Matrix Multiplication Optimization Approach for Privacy-Preserving Machine Learning,” IEEE Conference on Dependable and Secure Computing (IEEE DSC), Tampa, FL USA, Nov. 2023
Hongxin Lin; Yunwei Chiu; Peiyuan Wu, “AMPose: Alternately Mixed Global-Local Attention Model for 3D Human Pose Estimation,” 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1-5, Rhodes Island, Greece, Jun. 2023
SY Yeh, FC Chang, CW Yueh, PY Wu, A Bernacchia, S Vakili, “Sample Complexity of Kernel-Based Q-Learning,” International Conference on Artificial Intelligence and Statistics, 453-469, Valencia, Apr. 2023
Fu-Chieh Chang, Farhang Nabiei, Pei-Yuan Wu, Alexandru Cioba, Sattar Vakili, Alberto Bernacchia, “Gradient Descent: Robustness to Adversarial Corruption,” NeurIPS Workshop: Optimization for Machine Learning, Dec. 2022
Gi-Luen Huang and Pei-Yuan Wu, “CTGAN: Cloud Transformer Generative Adversarial Network,” IEEE International Conference on Image Processing, Bordeaux, France, Oct. 2022
C. O. Ancuti et al., “NTIRE 2020 Challenge on NonHomogeneous Dehazing,” IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2029-2044, Seattle, WA, USA, Jun. 2020
M. Al-Rubaie, P. Y. Wu, J. M. Chang and S. Y. Kung, “Privacy-preserving PCA on horizontally-partitioned data,” 2017 IEEE Conference on Dependable and Secure Computing, 280-287, Taipei, Taiwan, Aug. 2017
S. Y. Kung and P. Y. Wu, “A Partial Cosine Kernel Approach to Incomplete Data Analysis,” Int’l Conf. on Advances in Big Data Analysis (ABDA), 95-101, Las Vegas, Jul. 2014
P. Y. Wu, C. C. Fang, J. M. Chang, S. Gilbert, and S. Y. Kung, “Cost-Effective Kernel Ridge Regression for Keystroke-Based Active Authentication System,” ICASSP, 6028-6032, Florence, May 2014
C. H. Lu and P. Y. Wu, “Using Density Invariant Graph Laplacian to Resolve Unobservable Parameters for Three-Dimensional Optical Bio-Imaging,” Int’l Conf. Acoustic, Speech, Signal Proc. (ICASSP), 1621-1625, Florence, May 2014
S. Y. Kung and P. Y. Wu, “Perturbation Regulated Kernel Regressors for Supervised Machine Learning,” Int’l Workshop on Machine Learning for Signal Processing (MLSP), 1-6, Santander, Sept. 2012
S. Y. Kung and P. Y. Wu, “On Efficient Learning and Classification Kernel Methods,” ICASSP, 2065-2068, Kyoto, Mar. 2012