•  12
    Universal coding and prediction on ergodic random points
    with Łukasz Dębowski
    Bulletin of Symbolic Logic 28 (3): 387-412. 2022.
    Suppose that we have a method which estimates the conditional probabilities of some unknown stochastic source and we use it to guess which of the outcomes will happen. We want to make a correct guess as often as it is possible. What estimators are good for this? In this work, we consider estimators given by a familiar notion of universal coding for stationary ergodic measures, while working in the framework of algorithmic randomness, i.e., we are particularly interested in prediction of Martin-L…Read more
  •  43
    Recently, a connection has been established between two branches of computability theory, namely between algorithmic randomness and algorithmic learning theory. Learning-theoretical characterizations of several notions of randomness were discovered. We study such characterizations based on the asymptotic density of positive answers. In particular, this note provides a new learning-theoretic definition of weak 2-randomness, solving the problem posed by (Zaffora Blando, Rev. Symb. Log. 2019). The …Read more
  •  10
    On unstable and unoptimal prediction
    Mathematical Logic Quarterly 65 (2): 218-227. 2019.