How to submit a research paper, a technical report, a grant proposal , or a curriculum vitae that respect imposed constraints such as formatting instructions and page limits? It is a challenging task, especially when coping with time pressure. In this work, we present VaryL A T E X, a solution based on variability, constraint programming , and machine learning techniques for documents written in L A T E X to meet constraints and deliver on time. Users simply have to annotate L A T E X source files with variability information, e.g., (de)activating portions of text, tuning figures' sizes, or tweaking line spacing. Then, a fully automated procedure learns constraints among Boolean and numerical values for avoiding non-acceptable paper variants, and finally, users can further configure their papers (e.g., aesthetic considerations) or pick a (random) paper variant that meets constraints, e.g., page limits. We describe our implementation and report the results of two experiences with VaryL A T E X.