We combine transition-based dependency parsing with a high performing but relatively underexplored machine learning technique, Robust Risk Minimization. During decoding, we judiciously prune the next parsing states using k-best ranking. Moreover, we apply a simple post-processing to ensure robustness. We evaluate our approach on the CoNLL’09 shared task English data and improve transition-based dependency parsing accuracy, reaching a labeled attachment score of 89.28%. We also have observed near quadratic average running time in practice for the algorithm.