Short Bio

Yao-Yuan is a Ph.D. student 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)





Robustness for Non-Parametric Classification: A Generic Attack and Defense
AISTATS 2020, Virtual, August 2020 [video] [slide]
A Closer Look at Accuracy vs. Robustness
ICML UDL 2020, Virtual, June 2020 [video] [slide]
Deep Learning with a Rethinking Structure for Multi-label Classification
ACML, Nagoya, Japan, November 2019 [slide] [poster]
Deep Learning with a Rethinking Structure for Multi-label Classification
ACML-Mol, Beijing, China, November 2018 [slide]
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
PAKDD, Melbourne, Australia, June 2018 [slide]
Cost-Sensitive Random Pair Encoding for Cost-Sensitive Multi-Label Classification
NTU Machine Learning Symposium, Taipei, Taiwan, December 2016 [slide]
Near-uniform Aggregation of Gradient Boosting Machines for KDD Cup 2015
KDD, Sydney, Australia, August 2015 [slide]
Introduction to Machine Learning: Teaching Machine to Read Gestures
SITCON, Taipei, Taiwan, March 2015 [slide]