29 citations · 195 across the 32 of their papers we have counts for
5 papers · 1 filter
FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
Puheng Li, James Zou, Linjun Zhang
Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guara…
C-Mixup: Improving Generalization in Regression
Huaxiu Yao, Yiping Wang, Linjun Zhang +2
Improving the generalization of deep networks is an important open challenge, particularly in domains without plentiful data. The mixup algorithm improves generalization by linearl…
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise
Haotian Ye, James Zou, Linjun Zhang
The existence of spurious correlations such as image backgrounds in the training environment can make empirical risk minimization (ERM) perform badly in the test environment. To ad…
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Zhun Deng, Jiayao Zhang, Linjun Zhang +4
Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in ce…
Improving Out-of-Distribution Robustness via Selective Augmentation
Huaxiu Yao, Yu Wang, Sai Li +4
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shift is a common problem in real-world app…