We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible. The need to perform softmax iteratively on entire vector scores is avoided using tournament-style selection. As result, much better approximation and lower computational cost achieved compared previous approach.

This paper presents a novel approach to top-k ranking Bayesian optimization (top-k BO) which is practical and significant generalization of preferential BO handle tie/indifference observations. We first design surrogate model that not only capable catering the above observations, but also supported by classic random utility model. Another equally important contribution introduction information-...

We propose an indexing scheme for top-k shortestpath distance queries on graphs, which is useful in a wide range of important applications such as networkaware searches and link prediction. While many efficient methods for answering standard (top-1) distance queries have been developed, none of these methods are directly extensible to top-k distance queries. We develop a new framework for top-k...