PAC-Bayesian Theory for Transductive Learning

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PAC-Bayesian Theory Meets Bayesian Inference (2016, neutral): “However, despite the amount of work dedicated to this statistical learning theory—many authors improved the initial results [9, 29, 33, 43, 48] and/or generalized them for various machine learning setups [4, 14, 20, 28, 38, 44, 45, 46]—it is mostly used as a frequentist method.”
Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015, background): “…in the recent years several authors applied this theory to many more complex paradigms: Transductive Learning (Derbeko et al., 2004; Catoni, 2007; Bégin et al., 2014), Domain Adaptation (Germain et al., 2013), Density Estimation (Seldin and Tishby, 2009; Higgs and Shawe-Taylor, 2010),…”
An introduction to domain adaptation and transfer learning (2018, background): “Nonetheless, it is possible to derive generalization error bounds if more is known on the relationship between S and T [4, 17, 29, 46, 44, 43, 141, 18, 16, 212, 74, 15, 75].”
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach (2018, background): “See Bégin et al. (2014); Laviolette (2017) for a uniﬁed treatment of PAC-Bayesian bounds.”
PAC-Bayesian Bounds based on the Rényi Divergence (2016, unknown): “[2004] and Bégin et al. [2014]. The latter also experiment with other -functions in the transductive setting, such as the variation distance”