Short Bio
Yao-Yuan is a Ph.D. candidate in the Computer Science and Engineering department of UCSD. He is currently working with Professor Kamalika Chaudhuri on adversarial machine learning. Prior to joining UCSD, he received his B.S. in Computer Science from National Taiwan University in 2016.
Here are some problems he previously worked on:
- Cost-sensitive multi-label classification
- Active learning
- Brain decoding with electroencephalogram (EEG)
News
- Our work "Connecting Interpretability and Robustness in Decision Trees through Separation" get accepted into ICML 2021
- Our work "A Closer Look at Accuracy vs. Robustness" get accepted into NeurIPS 2020
- Our work "Robustness for Non-Parametric Methods: A Generic Attack and Defense" get accepted into AISTATS 2020
- I am presenting my work at the Machine Learning with Guarantees workshop at NeurIPS 2019
- I am presenting my work at ACML 2019
Experiences
- Ph.D., Computer Science and Engineering, University of California, San Diego, September 2018 - Current
- M.S. (transferred to Ph.D.), Computer Science and Engineering, University of California, San Diego, September 2017 - September 2018
- Alternative service of education, Minister of Education, Aug 2016 - June 2017
- B.S., Department of Computer Science and Information Engineering, National Taiwan University, September 2012 - June 2016
- Lab member, Modeling & Informatics Lab, 2014 - 2016
- Research Assistant, Computational Learning Lab , 2013 - 2016
- Lab member, Fast Crypto Lab , 2012 - 2014
Publications
-
torchaudio: Building Blocks for Audio and Speech Processing
Yao-Yuan Yang*, Moto Hira*, Zhaoheng Ni*, Artyom Astafurov, Caroline Chen, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jeff Hwang, Ji Chen, Peter Goldsborough, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, and Vincent Quenneville-Bélair, in submission, 2021, [bib] [code]
-
Hide and seek: Choices of virtual backgrounds in video chats and their effects on perception
Angel Hsing-Chi Hwang, Cheng Yao Wang, Yao-Yuan Yang, and Andrea Stevenson Won, in Proceedings of the 2021 ACM Conference on Computer Supported Cooperative Work (CSCW), 2021, [bib] [pdf] [video] [url]
-
Connecting Interpretability and Robustness in Decision Trees through Separation
Michal Moshkovitz, Yao-Yuan Yang, and Kamalika Chaudhuri, in Proceedings of International Conference on Machine Learning (ICML), 2021, [bib] [arxiv] [code]
-
Robustness and Generalization to Nearest Categories
Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, and Kamalika Chaudhuri, in submission, 2020, [bib] [arxiv] [code]
-
A Closer Look at Accuracy vs. Robustness
Yao-Yuan Yang*, Cyrus Rashtchian*, Hongyang Zhang, Ruslan Salakhutdinov, and Kamalika Chaudhuri, in Proceedings of Neural Information Processing Systems (NeurIPS), 2020, [bib] [arxiv] [code]
-
Robustness for Non-Parametric Classification: A Generic Attack and Defense
Yao-Yuan Yang*, Cyrus Rashtchian*, Yizhen Wang, and Kamalika Chaudhuri, in Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2020, [bib] [arxiv] [code]
-
PABLO: Helping Novices Debug Python Code Through Data-Driven Fault Localization
Benjamin Cosman, Madeline Endres, Georgios Sakkas, Leon Medvinsky, Yao-Yuan Yang, Ranjit Jhala, Kamalika Chaudhuri, and Westley Weimer, in Proceedings of the Special Interest Group on Computer Science Education (SIGCSE), 2020, [bib] [pdf]
-
Deep Learning with a Rethinking Structure for Multi-label Classification
Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, and Hsuan-Tien Lin, in Proceedings of the Asian Conference on Machine Learning (ACML), 2019, [bib] [arxiv] [code]
-
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, and Hsuan-Tien Lin, in Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018, [bib] [arxiv] [pdf] [code]
-
libact: Pool-based Active Learning in Python
Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, and Hsuan-Tien Lin, 2017, [bib] [arxiv] [code]