Kati8/ICLR_2023_2025 huggingface dataset:
authors: Yilan Zhang
authors: Yingxue Xu
authors: Jianqi Chen
authors: Fengying Xie
reviews: 
Comments: The authors introduce a novel framework, Prototypical Information Bottlenecking and Disentangling (PIBD), comprising a Prototypical Information Bottleneck (PIB) module for handling intra-modal redundancy and a Prototypical Information Disentanglement (PID) module for addressing inter-modal redundancy. Extensive experiments conducted on five cancer benchmark datasets establish their method's superiority over other approaches.
Strengths: (1) The concept of Information Bottleneck (IB) is intriguing, as it offers a promising solution to eliminate unnecessary redundancy, and the PIB module effectively addresses computational challenges.
(2) The technical details are presented with clarity and precision.
(3) The thoroughness of the experiments conducted to validate the effectiveness of each component is commendable.
(4) The quality of the visual presentations is noteworthy.
Weaknesses: (1) The Prototypical Information Disentanglement (PID) module could benefit from improved clarity in its description.
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: This paper introduces a novel approach to predicting survival outcome by combining histology and genomics data. The model incorporates two novel components based on information bottlenecks in information theory: prototypical information bottleneck and prototypical information disentanglement.
Strengths: The problem is well defined and the introduced method is appropriate with detailed explanation and evaluation. Results represent a meaningful improvement on prior state of the art approaches. The analysis is thorough and builds upon established practices in the literature. Although the advances over the state of the art are modest, it does represent a consistent improvement compared to other methods across all datasets.
Weaknesses: There are very few weaknesses with this paper. 

The evaluation could be improved by adding a “naive” combination of the risk scores from genomics and histology. This could be performed by taking the best performing models which look at an individual modality – i.e. SNNTrans and CLAM-MB – and then combining the predictions of these models using a Cox proportional hazards model against the original survival outcome data. This would act as a suitable baseline to see to what extent learning the combined risk prediction model as described in this work outperforms a naive approach to combining the risk prediction across the two modalities. This would allow the reader to fully understand the added benefit of the model described over the other approaches.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 4 excellent
Clarity: 4 excellent