We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. Thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability.

我们研究了基于功能的新闻企业问题，其中决策者可以访问包括需求观察和外源特征组成的历史数据。在这种情况下，我们研究了功能选择，旨在得出具有改进样本外部性能的稀疏，可解释的模型。到目前为止，最新的方法利用正则化，这会惩罚所选特征的数量或解决方案向量的规范。作为替代方案，我们介绍了一种新型的双层编程公式。高级问题选择了一部分功能，这些功能将基于固定验证集的订购决策的样本外成本估算最小化。下层问题仅使用上层选择的功能，了解训练集中决策功能的最佳系数。我们为Bilevel程序提供了混合整数线性程序重新制定，可以通过标准优化求解器求解为最佳性。我们的计算实验表明，该方法准确地恢复了几百个观察结果的实例中的基础真相。相反，基于正则化的技术通常在功能恢复时失败，或者需要数千个观察值才能获得相似的准确性。关于样本外的概括，我们实现了改进或可比的成本绩效。

由于其损耗函数的无限性，经典的铰链损耗支撑矢量机（SVM）模型对异常观测值敏感。为了解决这个问题，最近的研究集中在非凸损失函数上，例如硬质量损失，该损失将恒定的罚款与任何错误分类或细边样品内的样本相关联。应用此损失函数可为关键应用带来急需的鲁棒性，但它也导致NP硬化模型，这使训练变得困难，因为当前的精确优化算法显示有限的可伸缩性，而启发式方法无法始终找到高质量的解决方案。在这种背景下，我们提出了新的整数编程策略，这些策略可显着提高我们将硬利润SVM模型培训为全球最优性的能力。我们引入了一种迭代采样和分解方法，其中使用较小的子问题来分离组合弯曲器的切割。这些切割量在分支和切割算法中的使用，可以更快地收敛到全球最佳。通过对经典基准数据集的大量数值分析，我们的解决方案算法首次求解了117个新数据集，以达到最佳性，并在基准最困难的数据集的平均最佳差距中降低了50％。

Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep sequence models have costs that scale with the sequence length rather than the sequence complexity. In this work, we instead treat data sequences as observations from an underlying continuous-time process and learn how to efficiently discretize while retaining information about the full sequence. As a consequence of decoupling sequential information from its temporal discretization, our approach allows for greater compression rates and smaller computational complexity. Moreover, the continuous-time approach naturally allows us to decode at different time intervals. We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.

This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. By means of synthetic and real data experiments it is established that the proposed estimator can achieve, in many cases, better accuracy than a recently proposed loss-based estimator. We contribute to the literature on measuring uncertainty by extracting new indexes of low, medium and high economic policy uncertainty, using the probabilistic quantile factor methodology. Medium and high indexes have clear contractionary effects, while the low index is benign for the economy, showing that not all manifestations of uncertainty are the same.

We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights. As organism networks feature vastly more parameters than simpler architectures, we perform our initial experiments on an arithmetic task as well as on simplified MNIST-dataset classification as a collective. We observe that individual particle networks tend to specialise in either of the tasks and that the ones fully specialised in the secondary task may be dropped from the network without hindering the computational accuracy of the primary task. This leads to the discovery of a novel pruning-strategy for sparse neural networks

Overfitting is a problem in Convolutional Neural Networks (CNN) that causes poor generalization of models on unseen data. To remediate this problem, many new and diverse data augmentation methods (DA) have been proposed to supplement or generate more training data, and thereby increase its quality. In this work, we propose a new data augmentation algorithm: VoronoiPatches (VP). We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches. Unlike other DA methods, VP uses small convex polygon-shaped patches in a random layout to transport information around within an image. Sudden transitions created between patches and the original image can, optionally, be smoothed. In our experiments, VP outperformed current DA methods regarding model variance and overfitting tendencies. We demonstrate data augmentation utilizing non-linear re-combination of information within images, and non-orthogonal shapes and structures improves CNN model robustness on unseen data.

An optimal delivery of arguments is key to persuasion in any debate, both for humans and for AI systems. This requires the use of clear and fluent claims relevant to the given debate. Prior work has studied the automatic assessment of argument quality extensively. Yet, no approach actually improves the quality so far. Our work is the first step towards filling this gap. We propose the task of claim optimization: to rewrite argumentative claims to optimize their delivery. As an initial approach, we first generate a candidate set of optimized claims using a sequence-to-sequence model, such as BART, while taking into account contextual information. Our key idea is then to rerank generated candidates with respect to different quality metrics to find the best optimization. In automatic and human evaluation, we outperform different reranking baselines on an English corpus, improving 60% of all claims (worsening 16% only). Follow-up analyses reveal that, beyond copy editing, our approach often specifies claims with details, whereas it adds less evidence than humans do. Moreover, its capabilities generalize well to other domains, such as instructional texts.

We consider distributed learning in the presence of slow and unresponsive worker nodes, referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding redundantly assigns partial computations to the worker such that the overall result can be recovered from only the non-straggling workers. Gradient codes are designed to tolerate a fixed number of stragglers. Since the number of stragglers in practice is random and unknown a priori, tolerating a fixed number of stragglers can yield a sub-optimal computation load and can result in higher latency. We propose a gradient coding scheme that can tolerate a flexible number of stragglers by carefully concatenating gradient codes for different straggler tolerance. By proper task scheduling and small additional signaling, our scheme adapts the computation load of the workers to the actual number of stragglers. We analyze the latency of our proposed scheme and show that it has a significantly lower latency than gradient codes.

With the rise of AI and automation, moral decisions are being put into the hands of algorithms that were formerly the preserve of humans. In autonomous driving, a variety of such decisions with ethical implications are made by algorithms for behavior and trajectory planning. Therefore, we present an ethical trajectory planning algorithm with a framework that aims at a fair distribution of risk among road users. Our implementation incorporates a combination of five essential ethical principles: minimization of the overall risk, priority for the worst-off, equal treatment of people, responsibility, and maximum acceptable risk. To the best of the authors' knowledge, this is the first ethical algorithm for trajectory planning of autonomous vehicles in line with the 20 recommendations from the EU Commission expert group and with general applicability to various traffic situations. We showcase the ethical behavior of our algorithm in selected scenarios and provide an empirical analysis of the ethical principles in 2000 scenarios. The code used in this research is available as open-source software.