3 citations · 6 across the 6 of their papers we have counts for
6 papers
Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
Guang-Yuan Hao, Lars van der Laan, Aurélien Bibaut +1
We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environmen…
Taxonomy-Structured Domain Adaptation
Tianyi Liu, Zihao Xu, Hao He +3
Domain adaptation aims to mitigate distribution shifts among different domains. However, traditional formulations are mostly limited to categorical domains, greatly simplifying nua…
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…
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Baojun Wang, Zhao Zhang, Kun Xu +7
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks. However, previous works commonly have difficulty dealing…
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision
Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independe…
MIXGAN: Learning Concepts from Different Domains for Mixture Generation
Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new…