Daniel Kuhn, Tobias Sutter, Mengmeng Li
We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2021