We consider capacity maximization in wireless networks under adversarial interference conditions. There are n links, each consisting of a sender and a receiver, which repeatedly try to perform a successful transmission. In each time step, the success of attempted transmissions depends on interference conditions, which are captured by an interference model (e.g. the SINR model). Additionally, an adversarial jammer can render a (1-delta)-fraction of time steps unsuccessful. For this scenario, we analyze a framework for distributed learning algorithms to maximize the number of successful transmissions. Our main result is an algorithm based on no-regret learning converging to an O(1/delta)-approximation. It provides even a constant-factor approximation when the jammer exactly blocks a (1-delta)-fraction of time steps. In addition, we consider a stochastic jammer, for which we obtain a constant-factor approximation after a polynomial number of time steps. We also consider more general settings, in which links arrive and depart dynamically, and where each sender tries to reach multiple receivers. Our algorithms perform favorably in simulations.