Originality: Training of optimal decision trees is clearly a problem that has seen a lot of prior work. A distinguishing feature of this submission is that it focuses on optimal *sparse* decision trees for binary variables, and that the approach seems to be feasible in practice, which is achieved by a combination of analytical bounds that reduce the search space as well as efficient implementation techniques. The work builds upon the CORLES algorithm and its approach to creating optimal decision lists. However, the authors extend this approach to decision trees in a non-trivial manner that adds substantial novelty. Quality: The claims of the paper are very well supported by theoretical analysis as well as experiments.

Reviewers are very positive about the paper. The contribution is clear and significant. The paper should clearly be accepted. The authors should take into account all reviewers' comments when preparing the final version of their paper, as promised in their response, in particular the improvements suggested by reviewer 1 (as I agree that the paper is heavy on notation and not totally self-contained).

Although the paper is a good attempt at this space, and the messages should be echoed wide in the community, the paper could benefit from various improvements. Specifically, I am unsure if some of the performed experiments are supportive of the claims made in the paper. Details are as follows: Line 79: Authors discuss evaluating interventional distribution. But if the structure learning part is correct, then the learned distribution will also be correct as long as the parameterization is known or for discrete variables. After reading the rest, I guess authors are concerned about approximately learning the structure, and then depending on whether strong or weak edges are omitted can be determined by such an evaluation.

The reviewers agreed that this paper addresses an important notion that should be disseminated widely in the ML community working on causal learning. While some reviewers were concerned that sample size issues may lie at the root of some of the findings of the paper, most found that the papers' contribution is more foundational: is asks what types of questions and metrics should even be used when evaluating causal inference methods. Beyond the wide survey of existing practice, the proposal for interventional measures and the novel type of benchmark dataset proposed would be interesting and useful to the community.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

Summary and Contributions: This paper considers the problem of learning decision trees. You are given samples from a function f on the Boolean cube that is known to be computed by a size s decision tree. The goal is to produce a hypothesis h that is also a small decision tree and is close to f. It was known that simply looking at correlations is not a good idea, simple functions like parity of a few variables would defeat this algorithm. Indeed, I don't think there was any known algorithm that was guaranteed to return a "decision tree" of small size. This paper presents an algorithm of this type.

The setup is original and I see high value in the persistent-noise assumption worked out by the authors. I do have one main question to the authors and while I recommend this paper to be accepted based on significance and appearance of correctness, I do expect a very strong answer on this point for the score to remain high after rebuttal phase. The authors state in their experiment: "To ensure every pair of chemicals can be distinguished, we removed the chemicals that are not identifiable from each other." Well, for significance of the present work, we also need to know how the algorithms are going to behave in the worst-case if there are symmetries and this kind of preprocessing step is omitted. Note that the user would be happy with being presented a set of hypotheses and a certificate that no further test is available to distinguish among them.

All reviewers are positive or very positive about the paper and most reviewers were satisfied by the authors reponse. This is a clear accept. I however encourage the authors to take into account the reviewers comments to improve their paper, especially the (unanswered) issues raised by reviewer 4.

Stigma has emerged as one of the major obstacles to effectively diagnosing depression, as it prevents users from open conversations about their struggles. This requires advanced questioning skills to carefully probe the presence of specific symptoms in an unobtrusive manner. While recent efforts have been made on depression-diagnosis-oriented dialogue systems, they largely ignore this problem, ultimately hampering their practical utility. To this end, we propose a novel and effective method, UPSD$^{4}$, developing a series of strategies to promote a sense of unobtrusiveness within the dialogue system and assessing depression disorder by probing symptoms. We experimentally show that UPSD$^{4}$ demonstrates a significant improvement over current baselines, including unobtrusiveness evaluation of dialogue content and diagnostic accuracy. We believe our work contributes to developing more accessible and user-friendly tools for addressing the widespread need for depression diagnosis.

Additional Feedback: The paper is not interesting enough for a competitive conference. It is good to have these results in the literature, but I suggest to send it to a journal. Having read the reviews, and following the discussion, I still think that this does not below in a competitive conference. Indeed, as the authors stress in their response, the power of the result is due to the specific algorithm developed here. Nevertheless, I cannot be excited by it, given the monotonicity assumption and the fact that it applies only to the uniform distribution setting. I agree that it's an interesting result, but I think that it's not interesting enough nor important enough for a top conference.