39 citations · 110 across the 9 of their papers we have counts for
8 papers · 1 filter
Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent
Chi-Ning Chou, Oscar Uzdelewicz, Neng-Chun Chiu +2
Training loss and accuracy are the standard signals used to monitor generalization during deep neural network training. Two well-documented phenomena complicate this picture: in gr…
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu +2
Having similar behavior at training time and test time what we call a "What You See Is What You Get" (WYSIWYG) property is desirable in machine learning. Models trained wit…
Understanding Rare Spurious Correlations in Neural Networks
Yao-Yuan Yang, Chi-Ning Chou, Kamalika Chaudhuri
Neural networks are known to use spurious correlations such as background information for classification. While prior work has looked at spurious correlations that are widespread i…
Connecting Interpretability and Robustness in Decision Trees through Separation
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Recent research has recognized interpretability and robustness as essential properties of trustworthy classification. Curiously, a connection between robustness and interpretabilit…
Robustness for Non-Parametric Classification: A Generic Attack and Defense
Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang +1
Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or cla…
Deep Learning with a Rethinking Structure for Multi-label Classification
Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu +1
Multi-label classification (MLC) is an important class of machine learning problems that come with a wide spectrum of applications, each demanding a possibly different evaluation c…
libact: Pool-based Active Learning in Python
Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung +3
libact is a Python package designed to make active learning easier for general users. The package not only implements several popular active learning strategies, but also features…
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang +1
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the pred…