722 citations · 1.9k across the 39 of their papers we have counts for
10 papers · 1 filter
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…
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…
Causal Discovery in Physical Systems from Videos
Yunzhu Li, Antonio Torralba, Animashree Anandkumar +2
Causal discovery is at the core of human cognition. It enables us to reason about the environment and make counterfactual predictions about unseen scenarios that can vastly differ…
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…
Visual Grounding of Learned Physical Models
Yunzhu Li, Toru Lin, Kexin Yi +5
Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical…
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…