ARSM Gradient Estimator for Supervised Learning to Rank

Dadaneh Siamak Zamani
Dadaneh Siamak Zamani
Boluki Shahin
Boluki Shahin

ICASSP, pp. 3157-3161, 2019.

Cited by: 2|Views2
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Abstract:

We propose a new model for supervised learning to rank. In our model, the relevancy labels are are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to the multivariate categorical variables with an unbiased and low-variance gradi...More

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