Paper: Multi-Instance Learning Based Anomaly Detection Method for Sequence Data with Application to the Credit Card Delinquency Risk Control (ICLR-2024)

Reviewer comment (Novelty):
This area is well researched and authors fail to create a clear research gap

Author response:
The real world unsupervised learning problems like the credit card risk control studies are very complicated. Currently there is no specific approach to this problem. DAGMM seems to be one of the most adaptable method so far. It combining traditional unsupervised methods and deep auto encoders together, and achieved some good results. However, in practice, sample features are constructed artificially, which makes the representation of samples is not comprehensive enough. Therefore, the difference between abnormal samples and normal samples is limited, and the model can not distinguish them very well. Further more, this unsupervised algorithm cannot use the overdue information effectively because of the absence of abnormal labels for transactions. MILAD are designed for solving the anomaly detection problems in these applications similar to the credit card risk control study.

Reviewer comment (Experiments):
Why was DAGMM chosen for comparative analysis?

Author response:
DAGMM is the most commonly used method with convincible performance in this area so far.

Reviewer comment (Novelty):
MILAD novelty is what exactly? How does it differ from other similar methods?

Author response:
The real world unsupervised learning problems like the credit card risk control studies are very complicated. Currently there is no specific approach to this problem. DAGMM seems to be one of the most adaptable method so far. It combining traditional unsupervised methods and deep auto encoders together, and achieved some good results. However, in practice, sample features are constructed artificially, which makes the representation of samples is not comprehensive enough. Therefore, the difference between abnormal samples and normal samples is limited, and the model can not distinguish them very well. Further more, this unsupervised algorithm cannot use the overdue information effectively because of the absence of abnormal labels for transactions. MILAD are designed for solving the anomaly detection problems in these applications similar to the credit card risk control study.

Reviewer comment (Reproducibility):
Line 105 -- it is not clear what kind of architecture the transformer 'T' has -- encoder/decoder, etc.

Author response:
To questions:  
1. The transformer network T is not encoder/decoder structure. The details of the transformer network T is introduced in the Appendix A.0.1;

Reviewer comment (Reproducibility):
It is not clear whether the attention network W is based on self-attention. For self-attention, a position encoding is also used. Does the position encoding apply to W if the attention is only among random samples?

Author response:
To questions:  
2. Yes, the network W is based on the self attention. There is no random sample procedure.

Reviewer comment (Theory):
Line 111-118 -- Under the Collective Assumption, are the samples in a bag not randomly selected? Since in this case we need to use self-attention, it might imply a dependency on sequence or the samples being related in some manner. Else, how would we apply self-attention to a random collection of samples that are not in any guaranteed order?

Author response:
To questions:  
3. The samples in a bag are not randomly selected.