12 citations · 33 across the 6 of their papers we have counts for
8 papers
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Ching-Yao Chuang, Stefanie Jegelka
Understanding generalization and robustness of machine learning models fundamentally relies on assuming an appropriate metric on the data space. Identifying such a metric is partic…
Robust Contrastive Learning against Noisy Views
Ching-Yao Chuang, R Devon Hjelm, Xin Wang +5
Contrastive learning relies on an assumption that positive pairs contain related views, e.g., patches of an image or co-occurring multimodal signals of a video, that share certain…
Measuring Generalization with Optimal Transport
Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald +2
Understanding the generalization of deep neural networks is one of the most important tasks in deep learning. Although much progress has been made, theoretical error bounds still o…
Fair Mixup: Fairness via Interpolation
Ching-Yao Chuang, Youssef Mroueh
Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups. Nevertheless, even though the constraints are…
Contrastive Learning with Hard Negative Samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra +1
How can you sample good negative examples for contrastive learning? We argue that, as with metric learning, contrastive learning of representations benefits from hard negative samp…
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
When machine learning models are deployed on a test distribution different from the training distribution, they can perform poorly, but overestimate their performance. In this work…