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Arsm Gradient Estimator For Supervised Learning To Rank

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IEEE ICASSP 2020 Virtual Conference May 2020

We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to

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Arsm Gradient Estimator For Supervised Learning To Rank

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We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to