50 citations · 110 across the 12 of their papers we have counts for
9 papers · 1 filter
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
Zihao Xu, Guang-Yuan Hao, Hao He +1
Previous studies have shown that leveraging domain index can significantly boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628). However, such domain indices ar…
Contactless Oxygen Monitoring with Gated Transformer
Hao He, Yuan Yuan, Ying-Cong Chen +2
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In…
Domain Adaptation with Factorizable Joint Shift
Hao He, Yuzhe Yang, Hao Wang
Existing domain adaptation (DA) usually assumes the domain shift comes from either the covariates or the labels. However, in real-world applications, samples selected from differen…
Graph-Relational Domain Adaptation
Zihao Xu, Hao He, Guang-He Lee +2
Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; t…
Addressing Feature Suppression in Unsupervised Visual Representations
Tianhong Li, Lijie Fan, Yuan Yuan +5
Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive…
Continuously Indexed Domain Adaptation
Hao Wang, Hao He, Dina Katabi
Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously ind…
Learning Compositional Koopman Operators for Model-Based Control
Yunzhu Li, Hao He, Jiajun Wu +2
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis. The Koopman operator theory lay…
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations
Yuhui Wang, Hao He, Xiaoyang Tan
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violat…
From Bayesian Sparsity to Gated Recurrent Nets
Hao He, Bo Xin, David Wipf
The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights.…