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We present applications of\nthis learning scenario to convex and non-convex objectives for structured\nprediction and analyze them as stochastic first-order methods. We present an\nexperimental evaluation on problems of natural language processing over\nexponential output spaces, and compare convergence speed across different\nobjectives under the practical criterion of optimal task performance on\ndevelopment data and the optimization-theoretic criterion of minimal squared\ngradient norm. 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