The problem of minimax robust coding of classes for multiple- access channels with uncertainty in their statistical description is addressed. We consider: (i) discrete memoryless multiple- access channels with uncertainty in the probability transition matrices and (ii) discrete-time stationary additive Gaussian multiple-access channels with spectral uncertainty. The uncertainty is modeled using classes determined by 2-alternating Choquet capacities. Both block codes and tree are considered. A robust maximum-likelihood decoding rule is derived which guarantees that, for all two-user channels in this uncertainty class and all pairs of code rates in a critical rate region, the average probability of decoding error for the ensemble of pairs of random block codes and the ensemble of pairs of random tree codes converges to zero exponentially with increasing blocked length or constraint regions of the class are then evaluated.