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20172022
most citedFrom Bayesian Sparsity to Gated Recurrent Nets

22 citations · 45 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.LG20221 cited

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…

cs.LG20223 cited

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…

cs.LG20208 cited

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…

cs.LG2019

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…

cs.LG20199 cited

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…

cs.LG201722 cited

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.…