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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper connects two previously described methods of multitask feature learning, one where regularization is applied within and across tasks separately, and one where the regularization is performed on the jointly learned parameter matrix. This paper proves that under specific parameter settings, the two formulations are equivalent. This paper is fairly clear, but I would have liked one final statement of the full form of problem 1 using problem 2's parameters (or vice versa) rather than the current stating of parameter equivalence. However, the proofs are nicely presented with sketches of the steps described beforehand to help the reader stitch together the parts.