This Appendix is divided into three sections. Assumption 1. Next, in Section B, complete proofs of all the lemmas and theorems are presented. Figure 1: The estimated constants C and λ on various datasets. Hence, the relaxed Multiclass Tsybakov Condition holds and the probability of the first term of Eq. 17 Hence, by applying Hoeffding's inequality [5], we have: For fair comparisons, we implement FPMC with PyTorch. Figure 6: The percentage of instances that are rectified with increasing epochs. Does every data instance matter?

However, users can be induced to click on items that are inconsistent with their true preferences, resulting in unreliable instances, i.e., mismatched input-target pairs.

This Appendix is divided into three sections. Assumption 1. Next, in Section B, complete proofs of all the lemmas and theorems are presented. Figure 1: The estimated constants C and λ on various datasets. Hence, the relaxed Multiclass Tsybakov Condition holds and the probability of the first term of Eq. 17 Hence, by applying Hoeffding's inequality [5], we have: For fair comparisons, we implement FPMC with PyTorch. Figure 6: The percentage of instances that are rectified with increasing epochs. Does every data instance matter?

However, users can be induced to click on items that are inconsistent with their true preferences, resulting in unreliable instances, i.e., mismatched input-target pairs.