37 citations · 151 across the 13 of their papers we have counts for
10 papers · 1 filter
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization
Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang +3
Recent works on over-parameterized neural networks have shown that the stochasticity in optimizers has the implicit regularization effect of minimizing the sharpness of the loss fu…
Debiasing Vision-Language Models via Biased Prompts
Ching-Yao Chuang, Varun Jampani, Yuanzhen Li +2
Machine learning models have been shown to inherit biases from their training datasets. This can be particularly problematic for vision-language foundation models trained on uncura…
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…
InfoOT: Information Maximizing Optimal Transport
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
Optimal transport aligns samples across distributions by minimizing the transportation cost between them, e.g., the geometric distances. Yet, it ignores coherence structure in the…
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…
Debiased Contrastive Learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen +2
A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar (n…
The Role of Embedding Complexity in Domain-invariant Representations
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
Unsupervised domain adaptation aims to generalize the hypothesis trained in a source domain to an unlabeled target domain. One popular approach to this problem is to learn domain-i…