欧几里得最短的路径问题（ESPP）是许多实际应用的精心研究的问题。最近，基于射线拍摄和多边形扫描，已经开发了一种新的高效在线方法Rayscan。在本文中，我们展示了如何通过仔细理解多边形扫描来改善Rayscan。我们还研究了如何在单源多目标方案中应用Rayscan，在扫描过程中，逻辑用于减少所需的射线射击数量。这种改进也有助于单个目标情况。我们将改进的Rayscan+与最新的ESPP算法进行比较，说明了情况更好的情况。

AI方法做出预测和决策的兴起导致人们对更有解释的人工智能（XAI）方法的迫切需求。 XAI的一种常见方法是产生事后解释，解释了为什么黑匣子ML模型做出一定的预测。正式的事后解释方法为为什么做出预测以及为什么不进行其他预测提供了简洁的理由。但是这些方法假定特征是独立且均匀分布的。尽管这意味着“为什么”解释是正确的，但它们可能比要求更长。这也意味着“为什么不”解释可能会被怀疑，因为他们所依赖的反例可能没有意义。在本文中，我们展示了如何运用背景知识来提供更简洁的“为什么”形式解释，这些解释大概是人类更容易解释的，并给出了更准确的“为什么不”解释。此外，我们还展示了如何使用现有的规则归纳技术从数据集中有效提取背景信息，以及如何报告使用哪些背景信息来做出解释，从而使人类是否怀疑解释的正确性，可以检查它。 。

决策树学习是机器学习中广泛使用的方法，在需要简洁明了的模型的应用中受到青睐。传统上，启发式方法用于快速生产具有相当高准确性的模型。然而，一个普遍的批评是，从精度和大小方面，所产生的树可能不一定是数据的最佳表示。近年来，这激发了最佳分类树算法的发展，这些算法与执行一系列本地最佳决策的启发式方法相比，在全球范围内优化决策树。我们遵循这一工作线，并提供了一种基于动态编程和搜索的最佳分类树的新颖算法。我们的算法支持对树的深度和节点数量的约束。我们方法的成功归因于一系列专门技术，这些技术利用了分类树独有的属性。传统上，最佳分类树的算法受到了高运行时的困扰和有限的可伸缩性，但我们在一项详细的实验研究中表明，我们的方法仅使用最先进的时间所需的时间，并且可以处理数十个数据集的数据集在数千个实例中，提供了几个数量级的改进，并特别有助于实现最佳决策树的实现。

This paper presents a novel approach to the acquisition of language models from corpora. The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of probabilistic concepts that used a tabular, attribute-value encoding of training cases and concepts, making it unsuitable for sequential input like language. In response, we explore three new extensions to Cobweb -- the Word, Leaf, and Path variants. These systems encode each training case as an anchor word and surrounding context words, and they store probabilistic descriptions of concepts as distributions over anchor and context information. As in the original Cobweb, a performance element sorts a new instance downward through the hierarchy and uses the final node to predict missing features. Learning is interleaved with performance, updating concept probabilities and hierarchy structure as classification occurs. Thus, the new approaches process training cases in an incremental, online manner that it very different from most methods for statistical language learning. We examine how well the three variants place synonyms together and keep homonyms apart, their ability to recall synonyms as a function of training set size, and their training efficiency. Finally, we discuss related work on incremental learning and directions for further research.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

The evaluation of abstractive summarization models typically uses test data that is identically distributed as training data. In real-world practice, documents to be summarized may contain input noise caused by text extraction artifacts or data pipeline bugs. The robustness of model performance under distribution shift caused by such noise is relatively under-studied. We present a large empirical study quantifying the sometimes severe loss in performance (up to 12 ROUGE-1 points) from different types of input noise for a range of datasets and model sizes. We then propose a light-weight method for detecting and removing such noise in the input during model inference without requiring any extra training, auxiliary models, or even prior knowledge of the type of noise. Our proposed approach effectively mitigates the loss in performance, recovering a large fraction of the performance drop, sometimes as large as 11 ROUGE-1 points.

Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to input text prompts, while consistent with input images. We present Imagen Editor, a cascaded diffusion model built, by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. In addition, Imagen Editor captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

在因果推理和强盗文献中，基于观察数据的线性功能估算线性功能的问题是规范的。我们分析了首先估计治疗效果函数的广泛的两阶段程序，然后使用该数量来估计线性功能。我们证明了此类过程的均方误差上的非反应性上限：这些边界表明，为了获得非反应性最佳程序，应在特定加权$ l^2 $中最大程度地估算治疗效果的误差。 -规范。我们根据该加权规范的约束回归分析了两阶段的程序，并通过匹配非轴突局部局部最小值下限，在有限样品中建立了实例依赖性最优性。这些结果表明，除了取决于渐近效率方差之外，最佳的非质子风险除了取决于样本量支持的最富有函数类别的真实结果函数与其近似类别之间的加权规范距离。