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The paper presents some extensions to the Pentina and Lampert's PAC-Bayesian analysis of "Lifelong Learning" problems (ICML 2014), where a learner must adapt to various tasks exploiting knowledge from previously seen ones. The main contributions are risk bounds dedicated to two scenarios where the observed task are not sampled independently from each other. Roughly speaking, the first scenario share similarities with domain adaptation (albeit the risk bound is given on an average of all possible domains, instead of on a specific target domain) and the second is quasi-identical (up to my knowledge) to distribution drift. In the first setting (Section 3), the authors cleverly reuse Ralaivola et al.'s chromatic PAC-Bayesian theory to represent dependencies between tasks. However, this result alone let me unsatisfied. I wonder to which extent this result can be useful to the ambitious "lifelong learning" problem the authors are interested in.