InProceedingsofthe Networkand Distributed System Security Symposium, volume 14, pages 23-26, 2014.

Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting.

We thank the reviewers for their feedback. Our'formulation is generic and task-agnostic and therefore has the potential'The model simplifies existing work' ( R1) and'has been applied to many loss functions and tasks without any change'The experiments cover different tasks and benchmark datasets' ( R3). 'It is misleading to claim that the paper is the first work using task-agnostic weights that do not require iterative W e do not make such a claim . We believe a simple and easy-to-use idea has potential for great impact. We review (in Section 2.1 and Section 1 from the supplementary) We therefore propose in Section 2.2 the Section 2.3); (2) handle both positive-and negative-valued losses (which justifies the squared regularizer log term'Does not brings notably new criteria in determining the sample weights' (R3.3). 'SuperLoss does not show an advantage on clean data' (R3.4).