TY - CPAPER AB - Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding, which provides strong guarantees for compression of small datasets — in contrast with more popular estimators whose guarantees hold only in the asymptotic limit. Here we consider a NMLbased decision strategy for supervised classification problems, and show that it attains heuristic PAC learning when applied to a wide variety of models. Furthermore, we show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios. AU - Rosas,FE AU - Mediano,PAM AU - Gastpar,M DO - 10.1109/itw46852.2021.9457579 EP - 5 PB - IEEE PY - 2021/// SP - 1 TI - Learning, compression, and leakage: Minimising classification error via meta-universal compression principles UR - http://dx.doi.org/10.1109/itw46852.2021.9457579 UR - https://ieeexplore.ieee.org/document/9457579 UR - http://hdl.handle.net/10044/1/90016 ER -