PAC-Bayesian Domain Adaptation Bounds for Multi-view learning
CoRR(2024)
摘要
This paper presents a series of new results for domain adaptation in the
multi-view learning setting. The incorporation of multiple views in the domain
adaptation was paid little attention in the previous studies. In this way, we
propose an analysis of generalization bounds with Pac-Bayesian theory to
consolidate the two paradigms, which are currently treated separately. Firstly,
building on previous work by Germain et al., we adapt the distance between
distribution proposed by Germain et al. for domain adaptation with the concept
of multi-view learning. Thus, we introduce a novel distance that is tailored
for the multi-view domain adaptation setting. Then, we give Pac-Bayesian bounds
for estimating the introduced divergence. Finally, we compare the different new
bounds with the previous studies.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要