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Stochastic Structured Prediction under Bandit Feedback
Artem Sokolov · Julia Kreutzer · Stefan Riezler · Christopher Lo

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #101

Stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations, the learner receives an input, predicts an output structure, and receives partial feedback in form of a task loss evaluation of the predicted structure. We present applications of this learning scenario to convex and non-convex objectives for structured prediction and analyze them as stochastic first-order methods. We present an experimental evaluation on problems of natural language processing over exponential output spaces, and compare convergence speed across different objectives under the practical criterion of optimal task performance on development data and the optimization-theoretic criterion of minimal squared gradient norm. Best results under both criteria are obtained for a non-convex objective for pairwise preference learning under bandit feedback.

Author Information

Artem Sokolov (Heidelberg University)
Julia Kreutzer (Heidelberg University)
Stefan Riezler (Heidelberg University)
Christopher Lo (Tufts University)

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