We study online packing integer programs, where the columns arrive one by one. Since optimal algorithms were found for the RANDOMORDER model – where columns arrive in random order – much focus of the area has been on less optimistic models. One of those models is the MIXED model, where some columns are adversarially ordered, while others come in random-order. Very few results are known for packing IPs in the MIXED model, which is the object of our study. We consider online IPs with d occupation dimensions (d packing constraints), each one with capacity (or right-hand side) B. We also assume all items rewards and occupations to be less or equal to 1. Our goal is to design an algorithm where the presence of adversarial columns has a limited effect on the algorithm s competitiveness relative to the random-order columns. Thus, we use OPTStoch – the offline optimal solution considering only the random-order part of the input – as a benchmark.We present an algorithm that, relative to OPTStoch, is (1−5 lambda− OBig O of epsilon)-competitive with high probability, where lambda is the fraction of adversarial columns. In order to achieve such a guarantee, we make use of a primal-dual algorithm where the decision variables are set by evaluating each item s reward and occupation according to the dual variables of the IP, like other algorithms for the RANDOMORDER model do. However, we can t hope to estimate those dual variables by solving a scaled version of problem, because they could easily be manipulated by an adversary in the MIXED model. Our solution was to use online learning techniques to learn all aspects of the dual variables in an online fashion, as the problem progresses.