We address the problem of optimizing over functions defined on node subsets in a graph. The optimization of such functions is often a non-trivial task given their combinatorial, black-box and expensive-to-evaluate nature. Although various algorithms have been introduced in the literature, most are either task-specific or computationally inefficient and only utilize information about the graph structure without considering the characteristics of the function. To address these limitations, we utilize Bayesian Optimization (BO), a sample-efficient black-box solver, and propose a novel framework for combinatorial optimization on graphs. More specifically, we map each k-node subset in the original graph to a node in a new combinatorial graph and adopt a local modeling approach to efficiently traverse the latter graph by progressively sampling its subgraphs using a recursive algorithm. Extensive experiments under both synthetic and real-world setups demonstrate the effectiveness of the proposed BO framework on various types of graphs and optimization tasks, where its behavior is analyzed in detail with ablation studies.

Large scale neural networks used in practice are highly over-parameterized with far more trainable model parameters compared to the number of training examples. Consequently, optimization objectives for learning such high capacity models have many global minima that fit training data perfectly. However, minimizing the training loss using specific optimization algorithms take us to not just any global minima, but some special global minima, e.g., global minima minimizing some regularizer R(β). In over-parameterized models, specially deep neural networks, much, if not most, of the inductive bias of the learned model comes from this implicit regularization from the optimization algorithm. Understanding the implicit bias, e.g., via characterizing R(β), is thus essential for understanding how and what the model learns.

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We thank all reviewers for their insightful comments and the time they have spent carefully reviewing the paper. Consistent among all reviewers is the comment that the paper could be improved with further experiments. In Appendix F.2, we introduce another operator that In response to Reviewer 2's comment regarding comparisons to schemes where the adaptive entropy coefficient is We will clarify this difference in the paper.

The total size of the compressed dataset is 1.33 TB, with the GRD component (including the DEMs and metadata) taking 705.8 GB and the SLC 492.6 GB. All code and data will be maintained at the project's repo. On the left hand side we present the post-event SAR image used for the prediction, captured in 22/05/2023, while on the right hand side the respective Sentinel-2 RGB image captured in 23/05/2023 (one day later). In Figure 1 we assess the performance of our best model, i.e. Unet-ResNet50, on the recent floods of Emiglia-Romana, Italy, which took place on May 2023.

Global flash floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand underscore the urgent need for precise flood mapping to guide restoration efforts, understand vulnerabilities, and prepare for future occurrences. While Synthetic Aperture Radar (SAR) remote sensing offers day-and-night, all-weather imaging capabilities, its application in deep learning for flood segmentation is limited by the lack of large annotated datasets. To address this, we introduce Kuro Siwo, a manually annotated multi-temporal dataset, spanning 43 flood events globally.

Just a few days after Google unveiled its AR smart glasses, a new report suggests Apple may soon release a similar product. The iPhone maker is apparently planning on launching its long-rumored smart glasses in 2026, Bloomberg reported on Thursday. According to the report, the glasses will have microphones, speakers, and cameras built in, with an emphasis on AI features. This would allow users to ask for directions, do live language translation, and listen to music, among other things. However, one thing to note is that these glasses will more than likely not feature AR support of any kind, unlike Google's recently announced Android XR glasses.

We thank the reviewers for their valuable feedback, comments, and suggestions. We will add this and show more steps on line 626 for the benefit of future readers.

Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. On May 6, the Texas House Committee on Public Education discussed S.B. 13, a bill seeking to remove from public school libraries and classrooms all "profane" and "indecent content." At the hearing, Republican Rep. Terri Leo-Wilson focused on the concern that the legislation could harm the transmission of cultural heritage by depriving students of "classics." She explained, using an adjective that in our current culture wars has come to describe a type of humanities education favored by conservatives, that her "kids were classically trained, so they had their graduation picture with all sorts of books … classic works of literature." When an activist commenting during the hearing remarked that among renowned writers, Toni Morrison's work is singularly "very sexualized," Leo-Wilson replied, without reference to any one book, "She might be famous, but that's not considered, I don't think, a classic."