I think this claim should be motivated well enough, at least to me it was not entirely clear why this is important. If authors can provide some scenarios which can help understand the claim, it will be beneficial for the readers. However, the experimental analysis can be improved. One of the baselines the authors consider is "SM", but do not mention the paper in which it is proposed. They should produce results on multiple real world datasets. However, authors do not compare the proposed model with state of the art graph based SSL methods like GAT (Velickovic et al., ICML 2018) etc. [Velickovic et al., ICML 2018] Graph Attention Networks 4. Minor points: -- "vertexes" - "vertices" -- Not sure if using gradient descent qualifies as a contribution.

This paper considers graph-based semi-supervised learning when missing labels are not missing at random (NMAR), in other words, the absence of a label is'nonignorable'. It introduces a graphical neural network that models the relationship between the presence or absence of a label and the labels of its neighbors. The paper also proves that this model is identifiable. The reviewers agree that studying NMAR labels in the context of neural graph embeddings is a novel and important topic. They make several suggestions for improvement, including comparing with recent work such as Velickovic et al., ICML 2018.

Additional Feedback: Detailed feedback: 1. Authors state that "[other] methods, despite presenting consistent results for various object categories, have also their own limitations such as discovering landmarks with no clear semantic meaning." This claim is rather strong since the proposed method also does not guarantee any clear semantic meaning for object landmarks discovered by their method. That work actually reports better accuracy on BBCPose than the proposed method and hence should be also included. Since the paper is trying to distinguish between "keypoints/landmarks" and "object landmarks" it would be helpful to have a clear definition and use them consistently. For example, in the introduction, the three words are used interchangeably but then in the section 3 "keypoints and landmarks" refer to very different entities than "object landmarks".

This submission proposes an approach to unsupervised object landmark discovery. It initially received four reviews with mixed positive and negative scores (6,7,5,5). The rebuttal addressed some of the remaining concerns, which resulted in an increase in scores to (7,7,6,6). For these reasons, the AC's recommendation is to accept this submission for presentation as a poster, with a request for the authors to carefully revise the manuscript for the camera ready version to address the remaining concerns of the reviewers and improve the presentation clarity.

A key limitation of this work is that their proposed network VADeR is always initialized with MOCO self-supervised pre-training. While this is benign for practical purposes, it does conflate the two methods, and also means that VADeR is trained for longer etc. Training randomly initialized network with the proposed method will provide crucial empirical evidence, and would only strengthen, not weaken the experiments and claims.

Few-shot learning (FSL) enables machine learning models to generalize effectively with minimal labeled data, making it crucial for data-scarce domains such as healthcare, robotics, and natural language processing. Despite its potential, FSL faces challenges including sensitivity to initialization, difficulty in adapting to diverse domains, and vulnerability to noisy datasets. To address these issues, this paper introduces Adaptive Few-Shot Learning (AFSL), a framework that integrates advancements in meta-learning, domain alignment, noise resilience, and multi-modal integration. AFSL consists of four key modules: a Dynamic Stability Module for performance consistency, a Contextual Domain Alignment Module for domain adaptation, a Noise-Adaptive Resilience Module for handling noisy data, and a Multi-Modal Fusion Module for integrating diverse modalities. This work also explores strategies such as task-aware data augmentation, semi-supervised learning, and explainable AI techniques to enhance the applicability and robustness of FSL. AFSL provides scalable, reliable, and impactful solutions for real-world, high-stakes domains.

The contributions of this paper are multi-dimensional and highly significant: (i) developing a set of benchmarks for a diverse prediction tasks, (ii) demonstrating the utility of incorporating the vast amount of unlabeled protein data to pre-train models via semi-supervised learning, and (iii) the unlabeled data and pre-trained models made publicly available. This work will make a significant impact on the field by establishing solid benchmarks and facilitate the introduction of challenging protein prediction tasks to the machine learning community. The paper is extremely clearly written, well-structured and very concise. All reviewers are satisfied by the author response.

This paper theoretically and empirically shows that guarantee of non-trivial adversarial robustness only requires more unlabeled data. The paper theoretically proves that under the Gaussian model, more unlabeled data is enough to certify small robust accuracy (1e-3 in the paper) by their robust self-training algorithm. It is a pleasure to read it. The main concern is that the connection between the theory and the experiment is loose. The theory has very strong assumptions on the true model (Gaussian model).

This paper presents a theoretical analysis on using unlabeled data (under a self-training scheme of a Gaussian model) to improve the robustness against adversarial noise, followed by a semi-supervised learning method to learn deep networks. The empirical results are state-of-the-art. However, this paper heavily overlaps with another paper "Are Labels Required for Improving Adversarial Robustness?". As a condition to accepting and including the paper in the proceedings, put the following disclaimer in the footnote on the first page: "The authors declare that the present paper is independent of "Are Labels Required for Improving Adversarial Robustness?"."

Originality: 7 Quality:8 Clarity: 4 Significance:7 Mixmatch combined a lot of classical extraordinary methods that used for semi-supervised learning and achieved state-of-the-art results by a large margin across many datasets and labeled data amounts. Compared to previous method, this method is not only a simple combination of different data augmentation methods and other methods, such as exponential model average (EMA), it also explores a path to fully combine the advantages of different methods. In short, this method is of course a big step for semi-supervised learning on image classification. However, the experiments on this paper still needs to be modified to be perfect and a fair comparison with previous paper, such as Mean-Teacher. Also, some small problems need to be fixed to be finally published.