Chun-Chen Tu
Chun-Chen Tu
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
A Dhurandhar, PY Chen, R Luss, CC Tu, P Ting, K Shanmugam, P Das
Advances in neural information processing systems 31, 2018
Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
CC Tu, P Ting, PY Chen, S Liu, H Zhang, J Yi, CJ Hsieh, SM Cheng
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 742-749, 2019
RACOON: A multiuser QoS design for mobile wireless body area networks
SH Cheng, CY Huang, CC Tu
Journal of medical systems 35, 1277-1287, 2011
Generating contrastive explanations with monotonic attribute functions
R Luss, PY Chen, A Dhurandhar, P Sattigeri, K Shanmugam, CC Tu
arXiv preprint arXiv:1905.12698 3, 2019
Leveraging latent features for local explanations
R Luss, PY Chen, A Dhurandhar, P Sattigeri, Y Zhang, K Shanmugam, ...
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
Identifying influential links for event propagation on twitter: a network of networks approach
PY Chen, CC Tu, P Ting, YY Lo, D Koutra, AO Hero
IEEE Transactions on Signal and Information Processing over Networks 5 (1 …, 2018
FEAST: An automated feature selection framework for compilation tasks
PS Ting, CC Tu, PY Chen, YY Lo, SM Cheng
arXiv preprint arXiv:1610.09543, 2016
Prediction with high dimensional regression via hierarchically structured Gaussian mixtures and latent variables
CC Tu, F Forbes, B Lemasson, N Wang
Journal of the Royal Statistical Society Series C: Applied Statistics 68 (5 …, 2019
Improving prediction efficacy through abnormality detection and data preprocessing
CC Tu, PY Chen, N Wang
IEEE Access 7, 103794-103805, 2019
Enhancing Prediction Efficacy with High-Dimensional Input Via Structural Mixture Modeling of Local Linear Mappings
Structured Mixture of linear mappings in high dimension
CC Tu, F Forbes, B Lemasson, N Wang
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