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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2018)
/EventType (Poster)
/Description-Abstract (The standard margin\055based structured prediction commonly uses a maximum loss over all possible structured outputs\056 The large\055margin formulation including latent variables not only results in a non\055convex formulation but also increases the search space by a factor of the size of the latent space\056 Recent work has proposed the use of the maximum loss over random structured outputs sampled independently from some proposal distribution\054 with theoretical guarantees\056 We extend this work by including latent variables\056 We study a new family of loss functions under Gaussian perturbations and analyze the effect of the latent space on the generalization bounds\056 We show that the non\055convexity of learning with latent variables originates naturally\054 as it relates to a tight upper bound of the Gibbs decoder distortion with respect to the latent space\056 Finally\054 we provide a formulation using random samples and relaxations that produces a tighter upper bound of the Gibbs decoder distortion up to a statistical accuracy\054 which enables a polynomial time evaluation of the objective function\056 We illustrate the method with synthetic experiments and a computer vision application\056)
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/Title (Learning latent variable structured prediction models with Gaussian perturbations)
/Date (2018)
/ModDate (D\07220190218220644\05508\04700\047)
/Published (2018)
/Type (Conference Proceedings)
/firstpage (3145)
/Book (Advances in Neural Information Processing Systems 31)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (S\056 Bengio and H\056 Wallach and H\056 Larochelle and K\056 Grauman and N\056 Cesa\055Bianchi and R\056 Garnett)
/Author (Kevin Bello\054 Jean Honorio)
/lastpage (3155)
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