52 citations · 102 across the 8 of their papers we have counts for
11 papers · 1 filter
Fairness via Adversarial Attribute Neighbourhood Robust Learning
Qi Qi, Shervin Ardeshir, Yi Xu +1
Improving fairness between privileged and less-privileged sensitive attribute groups (e.g, {race, gender}) has attracted lots of attention. To enhance the model performs uniformly…
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…
Why Does Multi-Epoch Training Help?
Yi Xu, Qi Qian, Hao Li +1
Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in e…
A Theoretical Analysis of Learning with Noisily Labeled Data
Yi Xu, Qi Qian, Hao Li +1
Noisy labels are very common in deep supervised learning. Although many studies tend to improve the robustness of deep training for noisy labels, rare works focus on theoretically…
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks
Asaf Noy, Yi Xu, Yonathan Aflalo +2
Deep neural networks' remarkable ability to correctly fit training data when optimized by gradient-based algorithms is yet to be fully understood. Recent theoretical results explai…
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
Zhuoning Yuan, Zhishuai Guo, Yi Xu +2
Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on…