66 citations · 300 across the 29 of their papers we have counts for
6 papers · 1 filter
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Han Zhao, Chen Dan, Bryon Aragam +3
A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invaria…
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Bo Li, Yezhen Wang, Shanghang Zhang +4
The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to…
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang +8
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively e…
Information Obfuscation of Graph Neural Networks
Peiyuan Liao, Han Zhao, Keyulu Xu +4
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes addit…
Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada +2
Since its inception, the neural estimation of mutual information (MI) has demonstrated the empirical success of modeling expected dependency between high-dimensional random variabl…
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Remi Tachet, Han Zhao, Yu-Xiang Wang +1
Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations. However, recent work has shown l…