Kati8/ICLR_2023_2025 huggingface dataset:
authors: Juntong Shi
authors: Minkai Xu
authors: Harper Hua
authors: Hengrui Zhang
authors: Stefano Ermon
authors: Jure Leskovec
reviews: 
Comments: This paper introduces TABDIFF, a joint diffusion framework which aims to handle data with both discrete and continuous data types. The authors validated the effectiveness of this framework through comprehensive experiments.
Strengths: 1.The method introduces a new approach by jointly learning the diffusion processes for discrete and continuous features, which is not explored in the previous work.

2. The adaptively learnable noise schedules look novel.

3.The paper provides extensive experimental evaluations across different benchmark datasets, thoroughly validating the method’s performance.
Weaknesses: In the title of this paper, it is claimed that this paper addresses multi-modal distributions. However, the paper does not clearly specify what multi-modal distribution it handles. Does it only refer to distributions with both discrete and continuous variables?
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: The paper introduces TabDiff, an innovative multi-modal diffusion model designed to generate high-quality synthetic tabular data. It addresses the challenges of heterogeneous tabular data, which includes both numerical and categorical features with complex interdependencies. TabDiff employs a joint continuous-time diffusion process with feature-wise learnable components, allowing it to handle different data modalities while adapting to the varying distributions of individual features.
Strengths: 1. Tabdiff introduces a unified joint continuous-time diffusion process with feature-wise learnable components for handling both numerical and categorical data is innovative and allows the model to adapt to the specific characteristics of different data distributions.
2. The paper provides a thorough evaluation of TabDiff on a wide range of metrics, offering a deep and detailed assessment of the model performance from multiple dimensions, which is highly commendable.
3. The paper emphasizes the importance of TabDiff in generating high-quality synthetic data that enables effective model training while ensuring privacy preservation. The authors validate this effectiveness through the use of various metrics, demonstrating that the model not only maintains data fidelity but also safeguards against privacy risks. It's a practical trial for addressing the growing need for privacy-conscious data generation in sensitive domains.
Weaknesses: 1. Although the paper does not explicitly highlight it, the complexity of TabDiff, particularly in handling high-dimensional features through diffusion, could result in increased computational costs compared to simpler models. It would be good to provide a comparison of generation or training speed metrics with other baselines to better illustrate this trade-off.

2. The performance drop of TabDDPM on the News and Diabetes datasets is surprising, especially given that other diffusion-based methods maintain stable results. This discrepancy is beyond expected boundaries, with precision and recall even dropping to 0 in Table 7 and Table 8, and similarly poor performance across other tables for these datasets. 
Thus, while I appreciate the authors' comprehensive comparison across various metrics with baselines, the paper lacks sufficient analysis and discussion of the experimental results, particularly in explaining the performance gaps between TabDiff and the baselines. It would strengthen the paper if the authors provided more detailed discussion, and offered insights into the experimental setup of the baselines.

3. The paper lacks sufficient explanation of the experimental setup for these baselines, which makes it harder to interpret the results. Providing more clarity on these aspects would strengthen the case for TabDiff's performance and offer a more comprehensive understanding of the comparative results.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper introduces an approach called TABDIFF, which develops a joint continuous-time diffusion process for mixed-type data in tabular datasets. The authors address the challenge of high disparity between different feature distributions by proposing feature-wise learnable diffusion processes. The key contribution is a hybrid diffusion process to handle numerical and categorical features.
Strengths: **1** The experiments are extensive, demonstrating the efficiency of the proposed method.

**2** Hybrid diffusion models are employed to handle mixed-type data in tabular datasets.
Weaknesses: **1** The authors claim that they explicitly tackle the feature-wise heterogeneity issue in the multi-modal diffusion process. However, the details about how to handle this problem is missing.

**2** Even though experiments show the efficiency of the proposed method, it lacks of the motivation of considering a masked diffusion for categorical features.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3