237 citations · 552 across the 17 of their papers we have counts for
6 papers · 1 filter
Annealing Self-Distillation Rectification Improves Adversarial Training
Yu-Yu Wu, Hung-Jui Wang, Shang-Tse Chen
In standard adversarial training, models are optimized to fit one-hot labels within allowable adversarial perturbation budgets. However, the ignorance of underlying distribution sh…
Towards Large Certified Radius in Randomized Smoothing using Quasiconcave Optimization
Bo-Han Kung, Shang-Tse Chen
Randomized smoothing is currently the state-of-the-art method that provides certified robustness for deep neural networks. However, due to its excessively conservative nature, this…
Fair Robust Active Learning by Joint Inconsistency
Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen +1
Fairness and robustness play vital roles in trustworthy machine learning. Observing safety-critical needs in various annotation-expensive vision applications, we introduce a novel…
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +3
Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible pertu…
Communication Efficient Distributed Agnostic Boosting
Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau
We consider the problem of learning from distributed data in the agnostic setting, i.e., in the presence of arbitrary forms of noise. Our main contribution is a general distributed…
An Online Boosting Algorithm with Theoretical Justifications
Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu
We study the task of online boosting--combining online weak learners into an online strong learner. While batch boosting has a sound theoretical foundation, online boosting deserve…