Thee Chanyaswad

According to our database1, Thee Chanyaswad authored at least 14 papers between 2016 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2019
Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing.
Proc. Priv. Enhancing Technol., 2019

RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release.
Proc. Priv. Enhancing Technol., 2019

2018
Privacy-Preserving Machine Learning via Data Compression & Differential Privacy
PhD thesis, 2018

Supervising Nyström Methods via Negative Margin Support Vector Selection.
CoRR, 2018

A Differential Privacy Mechanism Design Under Matrix-Valued Query.
CoRR, 2018

Outlier Removal for Enhancing Kernel-Based Classifier Via the Discriminant Information.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Multi-Kernel, Deep Neural Network and Hybrid Models for Privacy Preserving Machine Learning.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

MVG Mechanism: Differential Privacy under Matrix-Valued Query.
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018

2017
Collaborative PCA/DCA Learning Methods for Compressive Privacy.
ACM Trans. Embed. Comput. Syst., 2017

Coupling Dimensionality Reduction with Generative Model for Non-Interactive Private Data Release.
CoRR, 2017

Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning.
CoRR, 2017

Differential mutual information forward search for multi-kernel discriminant-component selection with an application to privacy-preserving classification.
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

A compressive multi-kernel method for privacy-preserving machine learning.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016
Discriminant-component eigenfaces for privacy-preserving face recognition.
Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016


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