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A Smoother Way to Train Structured Prediction Models

Krishna Pillutla, University of Washington
Thursday, March 14, 2019 - 2:00pm to 3:00pm
PDL C-036

Structured prediction is the task of forecasting highly structured outputs like sequences, where the output space is combinatorially large. A classical approach, called the structural support vector machine casts the the task of training the model as a nonsmooth minimization problem, whose first order oracle is implemented by solving a combinatorial optimization problem. In this talk, we will go over ways to smooth such a nonsmooth objective and discuss how to obtain faster nonsmooth optimization algorithms with the use of increasingly tighter smooth surrogates.

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