统计学学术速递[9.6]

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stat统计学，共计17篇

【1】 Simultaneous quantification and changepoint detection of point source gas emissions using recursive Bayesian inference 标题：基于递归贝叶斯推理的点源气体排放同时量化和变点检测 链接：https://arxiv.org/abs/2109.01603

作者：Amir Montazeri,Xiaochi Zhou,John D. Albertson 机构：Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA, School of Civil and Environmental Engineering 摘要：最近的调查结果表明，石油和天然气供应链中设备的异常运行条件占人为甲烷排放量的很大一部分。因此，有效缓解排放需要快速识别和修复故障设备造成的污染源。除了允许更频繁监视的传感技术的进步外，快速和经济高效地识别源还需要提供自动故障检测的计算框架。在此，我们提出了一种基于递归贝叶斯方案的转换点检测算法，该算法允许同时进行发射率估计和故障检测。提出的算法在一系列近场控制释放移动实验上进行了测试，结果表明，当发射率在突变后增加三倍时，成功检测到泄漏率的变化（>90%成功率）。此外，我们还证明了测量值的统计信息，如变异系数和范围，是该算法性能的良好预测器。最后，我们描述了该方法如何容易地适应由固定传感器测量的时间平均浓度数据，从而展示了其灵活性。 摘要：Recent findings suggest that abnormal operating conditions of equipment in the oil and gas supply chain represent a large fraction of anthropogenic methane emissions. Thus, effective mitigation of emissions necessitates rapid identification and repair of sources caused by faulty equipment. In addition to advances in sensing technology that allow for more frequent surveillance, prompt and cost-effective identification of sources requires computational frameworks that provide automatic fault detection. Here, we present a changepoint detection algorithm based on a recursive Bayesian scheme that allows for simultaneous emission rate estimation and fault detection. The proposed algorithm is tested on a series of near-field controlled release mobile experiments, with promising results demonstrating successful detection (>90% success rate) of changes in the leak rate when the emission rate is tripled after an abrupt change. Moreover, we show that the statistics of the measurements, such as the coefficient of variation and range are good predictors of the performance of the algorithm. Finally, we describe how this methodology can be easily adapted to suit time-averaged concentration data measured by stationary sensors, thus showcasing its flexibility.

【2】 Quantum support vector regression for disability insurance 标题：用于残疾保险的量子支持向量回归 链接：https://arxiv.org/abs/2109.01570

作者：Boualem Djehiche,Björn Löfdahl 摘要：我们提出了一种混合经典量子方法来模拟健康和残疾保险中的转移概率。logistic残疾起始概率的建模是一个支持向量回归问题。使用量子特征映射，将数据映射到属于量子特征空间的量子态，其中关联的内核由量子态之间的内积确定。这个量子核可以在量子计算机上有效地估计。我们在IBM Yorktown量子计算机上进行实验，将该模型与瑞典一家保险公司的残疾初始数据进行拟合。 摘要：We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data is mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company.

【3】 Variational Bayes algorithm and posterior consistency of Ising model parameter estimation 标题：变分Bayes算法与Ising模型参数估计的后验相合性 链接：https://arxiv.org/abs/2109.01548

作者：Minwoo Kim,Shrijita Bhattacharya,Tapabrata Maiti 机构： SHRIJITA BHATTACHARYA† AND TAPABRATA MAITI‡Department of Statistics and Probability, Michigan State University 备注：26 pages 摘要：伊辛模型起源于统计物理，广泛应用于空间数据建模和计算机视觉问题。然而，该模型的统计推断仍然具有挑战性，因为似然中的归一化常数具有难以处理的性质。在这里，我们使用伪似然法来研究具有完全指定耦合矩阵的双参数、逆温度和磁化伊辛模型的贝叶斯估计。我们发展了一种计算效率高的变分贝叶斯模型估计方法。在高斯平均场变分族下，我们得到了伪似然下变分后验概率的后验收缩率。我们还讨论了伪似然方法由于变分后验比真后验引起的损失。大量的模拟研究验证了平均场高斯和二元高斯族作为伊辛模型参数推断变分族的可能选择的有效性。 摘要：Ising models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains challenging due to intractable nature of the normalizing constant in the likelihood. Here, we use a pseudo-likelihood instead to study the Bayesian estimation of two-parameter, inverse temperature, and magnetization, Ising model with a fully specified coupling matrix. We develop a computationally efficient variational Bayes procedure for model estimation. Under the Gaussian mean-field variational family, we derive posterior contraction rates of the variational posterior obtained under the pseudo-likelihood. We also discuss the loss incurred due to variational posterior over true posterior for the pseudo-likelihood approach. Extensive simulation studies validate the efficacy of mean-field Gaussian and bivariate Gaussian families as the possible choices of the variational family for inference of Ising model parameters.

【4】 Teacher Mental Health During the COVID-19 Pandemic: Informing Policies to Support Teacher Well-being and Effective Teaching Practices 标题：冠状病毒大流行期间的教师心理健康：为支持教师福祉和有效教学实践的政策提供信息 链接：https://arxiv.org/abs/2109.01547

作者：Joseph M. Kush,Elena Badillo-Goicoechea,Rashelle J. Musci,Elizabeth A. Stuart 机构：Stuart, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health 摘要：虽然有研究2019冠状病毒疾病的教育影响的研究，但在整个大流行期间评估教师心理健康的实证研究却很少。利用一个大型国家数据集，目前的研究首先比较了流行期间pK-12教师和其他职业专业人员的心理健康结果。此外，我们还比较了在职教师和远程教师（n=131154）心理健康结果的患病率。研究结果表明，教师比其他职业的教师更关注心理健康，远程教师比亲自授课的教师更容易感到苦恼。讨论了政策影响，重点是提供支持以满足教师不断变化的需求。 摘要：While there is an emergence of research investigating the educational impacts of the COVID-19 pandemic, empirical studies assessing teacher mental health throughout the pandemic have been scarce. Using a large national dataset, the current study first compared mental health outcomes during the pandemic between pK-12 teachers and professionals in other occupations. Further, we compared the prevalence of mental health outcomes between in-person and remote teachers (n = 131,154). Findings indicated teachers reported greater mental health concerns than those in other professions, and that remote teachers reported significantly higher levels of distress than those teaching in-person. Policy implications are discussed, with a focus on providing support to meet the evolving needs of teachers.

【5】 Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review 标题：2020年2~5月第一波冠状病毒流行模型方法学综述 链接：https://arxiv.org/abs/2109.01450

作者：Marie Garin,Myrto Limnios,Alice Nicolaï,Ioannis Bargiotas,Olivier Boulant,Stephen Chick,Amir Dib,Theodoros Evgeniou,Mathilde Fekom,Argyris Kalogeratos,Christophe Labourdette,Anton Ovchinnikov,Raphaël Porcher,Camille Pouchol,Nicolas Vayatis 机构：Universit´e Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-, Gif-sur-Yvette, France, INSEAD, Boulevard de Constance, Fontainebleau, France, Smith School of Business, Queen’s University, Kingston, ON, K,L,N, Canada 摘要：我们回顾了COVID-19流行病在发病前几个月传播的流行病学模型：从二月到2020年5月。目的是提出一种方法学的综述，突出了以下特征：（i）流行病传播模型，（ii）干预策略的建模；（iii）流行病参数的模型和估计程序，以及（iv）所用数据的特征。我们最终根据理论背景、再现性、干预策略的纳入等标准从开放存取数据库中选择了80篇文章。这主要导致现象学、分区和个体水平的模型。提出了一种包括在线表单、Kibana接口和降价文档的数字伴侣。最后，这项工作提供了一个机会来见证科学界对这一独特情况的反应。 摘要：We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodological review that highlights the following characteristics: (i) the epidemic propagation models, (ii) the modeling of intervention strategies, (iii) the models and estimation procedures of the epidemic parameters and (iv) the characteristics of the data used. We finally selected 80 articles from open access databases based on criteria such as the theoretical background, the reproducibility, the incorporation of interventions strategies, etc. It mainly resulted to phenomenological, compartmental and individual-level models. A digital companion including an online sheet, a Kibana interface and a markdown document is proposed. Finally, this work provides an opportunity to witness how the scientific community reacted to this unique situation.

【6】 Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process 标题：将偏相关图和排列特征重要性与数据生成过程相关联 链接：https://arxiv.org/abs/2109.01433

作者：Christoph Molnar,Timo Freiesleben,Gunnar König,Giuseppe Casalicchio,Marvin N. Wright,Bernd Bischl 机构： BischlLudwig-Maximilian University Munich, K¨onigUniversity of Vienna 摘要：科学家和实践者越来越依赖机器学习来建模数据和得出结论。与统计建模方法相比，机器学习对数据结构（如线性）的明确假设更少。然而，它们的模型参数通常不能很容易地与数据生成过程相关联。为了了解模型关系，通常使用部分相关（PD）图和排列特征重要性（PFI）作为解释方法。然而，PD和PFI缺乏将其与数据生成过程联系起来的理论。我们将PD和PFI形式化为根植于数据生成过程中的基本真值估计的统计估计。我们表明，由于统计偏差、模型方差和蒙特卡罗近似误差，PD和PFI估计偏离了这一基本事实。为了解释PD和PFI估计中的模型方差，我们提出了学习者PD和基于模型修正的学习者PFI，并提出了修正方差和置信区间估计。 摘要：Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer explicit assumptions about data structures, such as linearity. However, their model parameters usually cannot be easily related to the data generating process. To learn about the modeled relationships, partial dependence (PD) plots and permutation feature importance (PFI) are often used as interpretation methods. However, PD and PFI lack a theory that relates them to the data generating process. We formalize PD and PFI as statistical estimators of ground truth estimands rooted in the data generating process. We show that PD and PFI estimates deviate from this ground truth due to statistical biases, model variance and Monte Carlo approximation errors. To account for model variance in PD and PFI estimation, we propose the learner-PD and the learner-PFI based on model refits, and propose corrected variance and confidence interval estimators.

【7】 Frequency-Severity Experience Rating based on Latent Markovian Risk Profiles 标题：基于潜在马尔可夫风险分布的频度-严重性体验评级 链接：https://arxiv.org/abs/2109.01413

作者：Robert Matthijs Verschuren 机构：∗, Amsterdam School of Economics, University of Amsterdam, . 摘要：奖金MALUS系统传统上考虑客户的索赔数量，不管它们的大小，即使这些组件在实践中是依赖的。我们提出了一种新的基于潜在马尔可夫风险模型的联合经验评级方法，以允许正或负的个体频率严重性依赖性。潜在轮廓在隐马尔可夫模型中随时间演化，以捕获客户索赔体验中的更新，从而使索赔数量和规模有条件地独立。我们表明，由此产生的风险溢价导致了标准可信度溢价的动态、索赔经验加权混合。所提出的方法被应用于荷兰汽车保险组合，并识别具有独特索赔行为的客户风险状况。这些概况反过来使我们能够更好地区分客户风险。 摘要：Bonus-Malus Systems traditionally consider a customer's number of claims irrespective of their sizes, even though these components are dependent in practice. We propose a novel joint experience rating approach based on latent Markovian risk profiles to allow for a positive or negative individual frequency-severity dependence. The latent profiles evolve over time in a Hidden Markov Model to capture updates in a customer's claims experience, making claim counts and sizes conditionally independent. We show that the resulting risk premia lead to a dynamic, claims experience-weighted mixture of standard credibility premia. The proposed approach is applied to a Dutch automobile insurance portfolio and identifies customer risk profiles with distinctive claiming behavior. These profiles, in turn, enable us to better distinguish between customer risks.

【8】 Sample Noise Impact on Active Learning 标题：样本噪声对主动学习的影响 链接：https://arxiv.org/abs/2109.01372

作者：Alexandre Abraham,Léo Dreyfus-Schmidt 机构：and L´eo Dreyfus-Schmidt,r,´,´,´,s, Dataiku, Paris, France 备注：None 摘要：本研究探讨了噪音样本选择对主动学习策略的影响。我们在合成问题和实际使用案例中都表明，样本噪声知识可以显著提高主动学习策略的性能。在先前工作的基础上，我们提出了一种稳健的取样器，即增量加权K-均值，它对合成任务带来了显著的改进，但对现实生活中的任务只有轻微的提升。我们希望本文提出的问题能引起社区的兴趣，并为主动学习研究开辟新的途径。 摘要：This work explores the effect of noisy sample selection in active learning strategies. We show on both synthetic problems and real-life use-cases that knowledge of the sample noise can significantly improve the performance of active learning strategies. Building on prior work, we propose a robust sampler, Incremental Weighted K-Means that brings significant improvement on the synthetic tasks but only a marginal uplift on real-life ones. We hope that the questions raised in this paper are of interest to the community and could open new paths for active learning research.

【9】 Regularized tapered sample covariance matrix 标题：正则化渐变样本协方差矩阵 链接：https://arxiv.org/abs/2109.01353

作者：Esa Ollila,Arnaud Breloy 机构： Ollila is with the Department of Signal Processing and Acoustics, AaltoUniversity, University Paris Nanterrre 摘要：协方差矩阵锥在信号处理和相关领域有着悠久的历史。应用实例包括自回归模型（促进带状结构）或波束形成（加宽与干扰源相关的光谱零宽度）。在本文中，重点是高维设置，其中维度$p$较高，而数据纵横比$n/p$较低。我们提出了一种称为Tabasco（锥形或带状收缩协方差矩阵）的估计器，该估计器将锥形样本协方差矩阵收缩为一个标度单位矩阵。我们推导了最优和估计（数据自适应）正则化参数，这些参数旨在最小化所提出的收缩估计量和真实协方差矩阵之间的均方误差（MSE）。这些参数是在一般假设下得出的，即数据是从具有有限四阶矩的未指定椭圆对称分布中采样的（同时讨论实值和复数情况）。仿真研究表明，所提出的Tabasco在不同的设置下优于所有竞争性的渐减协方差矩阵估计。空时自适应处理（STAP）应用也说明了所提出的估计器在实际信号处理装置中的优势。 摘要：Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer). In this paper, the focus is on high-dimensional setting where the dimension $p$ is high, while the data aspect ratio $n/p$ is low. We propose an estimator called Tabasco (TApered or BAnded Shrinkage COvariance matrix) that shrinks the tapered sample covariance matrix towards a scaled identity matrix. We derive optimal and estimated (data adaptive) regularization parameters that are designed to minimize the mean squared error (MSE) between the proposed shrinkage estimator and the true covariance matrix. These parameters are derived under the general assumption that the data is sampled from an unspecified elliptically symmetric distribution with finite 4th order moments (both real- and complex-valued cases are addressed). Simulation studies show that the proposed Tabasco outperforms all competing tapering covariance matrix estimators in diverse setups. A space-time adaptive processing (STAP) application also illustrates the benefit of the proposed estimator in a practical signal processing setup.

【10】 Statistical Estimation and Inference via Local SGD in Federated Learning 标题：联合学习中基于局部SGD的统计估计和推理 链接：https://arxiv.org/abs/2109.01326

作者：Xiang Li,Jiadong Liang,Xiangyu Chang,Zhihua Zhang 摘要：联邦学习（FL）使大量边缘计算设备（如手机）在不共享数据的情况下联合学习全局模型。在FL中，数据以高度异构的分散方式生成。本文研究如何在联邦环境下进行统计估计和推理。我们分析了所谓的局部SGD，这是一种使用间歇通信来提高通信效率的多轮估计过程。我们首先建立了一个{\it泛函中心极限定理}，证明了局部SGD的平均迭代弱收敛于重标度布朗运动。接下来，我们将提供两种迭代推理方法：{\it plug-in}和{\it random scaling}。随机标度通过使用整个局部SGD路径上的信息来构造用于推理的渐近关键统计量。这两种方法都是有效的通信方法，适用于在线数据。我们的理论和实证结果表明，本地SGD同时实现了统计效率和通信效率。 摘要：Federated Learning (FL) makes a large amount of edge computing devices (e.g., mobile phones) jointly learn a global model without data sharing. In FL, data are generated in a decentralized manner with high heterogeneity. This paper studies how to perform statistical estimation and inference in the federated setting. We analyze the so-called Local SGD, a multi-round estimation procedure that uses intermittent communication to improve communication efficiency. We first establish a {\it functional central limit theorem} that shows the averaged iterates of Local SGD weakly converge to a rescaled Brownian motion. We next provide two iterative inference methods: the {\it plug-in} and the {\it random scaling}. Random scaling constructs an asymptotically pivotal statistic for inference by using the information along the whole Local SGD path. Both the methods are communication efficient and applicable to online data. Our theoretical and empirical results show that Local SGD simultaneously achieves both statistical efficiency and communication efficiency.

【11】 Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions 标题：作物制图的两个转变：利用综合作物统计数据改进新区域的卫星地图 链接：https://arxiv.org/abs/2109.01246

作者：Dan M. Kluger,Sherrie Wang,David B. Lobell 机构：a Department of Statistics, Sequoia Hall, Mail Code , Jane Stanford Way, Stanford, University, Stanford, CA ,-, United States of America, b Department of Earth System Science and Center on Food Security and the Environment, Encina 备注：None 摘要：农田一级的作物类型制图对于农业监测的各种应用至关重要，卫星图像正成为制作作物类型地图的日益丰富和有用的原始输入。尽管如此，在许多地区，利用卫星数据绘制作物类型图仍然受到缺乏用于训练监督分类模型的田间作物标签的限制。当一个区域中没有可用的训练数据时，可以传输在类似区域中训练的分类器，但作物类型分布的变化以及区域之间特征的转换会导致分类精度降低。我们提出了一种方法，通过考虑这两种类型的偏移，使用聚合级作物统计来校正分类器。为了调整作物类型组成的变化，我们提出了一种方案，用于适当地重新加权分类器输出的每个类别的后验概率。为了调整特征中的偏移，我们提出了一种估计和去除平均特征向量中线性偏移的方法。我们证明，当使用线性判别分析（LDA）绘制法国西塔尼省和肯尼亚西部省的作物类型时，该方法可显著提高总体分类精度。当使用LDA作为我们的基本分类器时，我们发现在法国，我们的方法使11个不同训练部门的误分类率降低了2.8%至42.2%（平均值=21.9%），在肯尼亚，三个训练区域的误分类率分别降低了6.6%、28.4%和42.7%。虽然我们的方法在统计学上是由LDA分类器驱动的，但它可以应用于任何类型的分类器。作为一个例子，我们展示了它在改进随机森林分类器中的成功应用。 摘要：Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between regions lead to reduced classification accuracy. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for these two types of shifts. To adjust for shifts in the crop type composition we present a scheme for properly reweighting the posterior probabilities of each class that are output by the classifier. To adjust for shifts in features we propose a method to estimate and remove linear shifts in the mean feature vector. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya. When using LDA as our base classifier, we found that in France our methodology led to percent reductions in misclassifications ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training departments, and in Kenya the percent reductions in misclassification were 6.6%, 28.4%, and 42.7% for three training regions. While our methodology was statistically motivated by the LDA classifier, it can be applied to any type of classifier. As an example, we demonstrate its successful application to improve a Random Forest classifier.

【12】 Robust confidence distributions from proper scoring rules 标题：基于适当评分规则的稳健置信分布 链接：https://arxiv.org/abs/2109.01219

作者：Erlis Ruli,Laura Ventura,Monica Musio 机构：ARTICLE HISTORY 备注：submitted 摘要：置信分布是基于参数统计模型的感兴趣参数的分布。因此，它与Bayesian的后验分布具有相同的作用，因为它允许到达点估计，评估其精度，建立测试以及证据措施，推导置信区间，将感兴趣的参数与其他研究中的其他参数进行比较，导出置信分布的一般方法是基于经典关键量及其精确或近似分布。然而，在观测数据中存在模型错误说明或离群值的情况下，经典关键量以及置信度分布可能不准确。本文的目的是讨论稳健置信分布的推导和应用。特别地，我们讨论了一种基于Tsallis评分规则的通用方法，以计算稳健的置信度分布。针对实际中经常遇到的两样本异方差比较、接收机工作特性曲线和回归模型等问题，给出了算例和仿真结果。 摘要：A confidence distribution is a distribution for a parameter of interest based on a parametric statistical model. As such, it serves the same purpose for frequentist statisticians as a posterior distribution for Bayesians, since it allows to reach point estimates, to assess their precision, to set up tests along with measures of evidence, to derive confidence intervals, comparing the parameter of interest with other parameters from other studies, etc. A general recipe for deriving confidence distributions is based on classical pivotal quantities and their exact or approximate distributions. However, in the presence of model misspecifications or outlying values in the observed data, classical pivotal quantities, and thus confidence distributions, may be inaccurate. The aim of this paper is to discuss the derivation and application of robust confidence distributions. In particular, we discuss a general approach based on the Tsallis scoring rule in order to compute a robust confidence distribution. Examples and simulation results are discussed for some problems often encountered in practice, such as the two-sample heteroschedastic comparison, the receiver operating characteristic curves and regression models.

【13】 Evaluating the Use of Generalized Dynamic Weighted Ordinary Least Squares for Individualized HIV Treatment Strategies 标题：评价广义动态加权普通最小二乘法在HIV个体化治疗策略中的应用 链接：https://arxiv.org/abs/2109.01218

作者：Larry Dong,Erica E. M. Moodie,Laura Villain,Rodolphe Thiébaut 机构：and Rodolphe Thi´ebaut, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, University of Bordeaux, INSERM U, Bordeaux Population Health, Inria SISTM 摘要：动态治疗模式（DTR）是精确医学中的一种统计范式，旨在通过个体化治疗优化患者结果。在最简单的情况下，DTR可能只需要做出一个决定；这种特殊情况称为个体化治疗规则（ITR），通常用于最大化短期回报。广义动态加权普通最小二乘法（G-dWOLS）是一种DTR估计方法，具有理论上的优势，例如决策规则中参数估计的双重鲁棒性，最近已被扩展，以适应分类处理。在这项工作中，G-dWOLS被应用于纵向数据以估计最佳ITR，这在仿真中得到了验证。然后将这种新方法应用于受HIV影响的人群，由此设计了用于施用白细胞介素7（IL-7）的ITR，以最大限度地延长CD4负荷高于健康阈值（500个细胞/$\mu$L）的持续时间，同时防止施用不必要的注射。 摘要：Dynamic treatment regimes (DTR) are a statistical paradigm in precision medicine which aim to optimize patient outcomes by individualizing treatments. At its simplest, a DTR may require only a single decision to be made; this special case is called an individualized treatment rule (ITR) and is often used to maximize short-term rewards. Generalized dynamic weighted ordinary least squares (G-dWOLS), a DTR estimation method that offers theoretical advantages such as double robustness of parameter estimators in the decision rules, has been recently extended to now accommodate categorical treatments. In this work, G-dWOLS is applied to longitudinal data to estimate an optimal ITR, which is demonstrated in simulations. This novel method is then applied to a population affected by HIV whereby an ITR for the administration of Interleukin 7 (IL-7) is devised to maximize the duration where the CD4 load is above a healthy threshold (500 cells/$\mu$L) while preventing the administration of unnecessary injections.

【14】 Multi-agent Natural Actor-critic Reinforcement Learning Algorithms 标题：多智能体自然行动者-批评型强化学习算法 链接：https://arxiv.org/abs/2109.01654

作者：Prashant Trivedi,Nandyala Hemachandra 机构：Industrial Engineering and Operations Research, Indian Institute of Technology Bombay India 备注：38 pages 摘要：单智能体和多智能体行动者批评算法都是一类重要的强化学习算法。在这项工作中，我们提出了三种完全分散的多智能体自然行动者-批评家（MAN）算法。代理人的目标是集体学习一个联合策略，使这些代理人的平均长期回报之和最大化。在没有中央控制器的情况下，代理通过时变通信网络将信息传递给其邻居，同时保护隐私。我们证明了所有3个MAN算法都收敛到了对应于演员更新的ODE的全局渐近稳定点；它们使用线性函数近似。我们使用Fisher信息矩阵来获得自然梯度。Fisher信息矩阵捕获连续迭代中策略之间Kullback-Leibler（KL）散度的曲率。我们还证明了连续迭代策略之间KL散度的梯度与目标函数的梯度成正比。我们的MAN算法确实使用了目标函数梯度的这种表示。在Fisher信息矩阵的某些条件下，我们证明了在每次迭代中，通过MAN算法得到的最优值都比使用标准梯度的multi-agent-actor-critic（MAAC）算法得到的最优值要好。为了验证我们提出的算法的有效性，我们在双车道交通网络上实现了所有3个MAN算法，以减少平均网络拥塞。我们观察到2人算法的平均拥塞减少了近25%；另一种算法的平均拥塞与MAAC算法是一致的。我们还考虑了一个通用的15代理MARL；MAN算法的性能与MAAC算法一样好。我们将MAN算法更好的性能归因于它们使用了上述表示。 摘要：Both single-agent and multi-agent actor-critic algorithms are an important class of Reinforcement Learning algorithms. In this work, we propose three fully decentralized multi-agent natural actor-critic (MAN) algorithms. The agents' objective is to collectively learn a joint policy that maximizes the sum of averaged long-term returns of these agents. In the absence of a central controller, agents communicate the information to their neighbors via a time-varying communication network while preserving privacy. We prove the convergence of all the 3 MAN algorithms to a globally asymptotically stable point of the ODE corresponding to the actor update; these use linear function approximations. We use the Fisher information matrix to obtain the natural gradients. The Fisher information matrix captures the curvature of the Kullback-Leibler (KL) divergence between polices at successive iterates. We also show that the gradient of this KL divergence between policies of successive iterates is proportional to the objective function's gradient. Our MAN algorithms indeed use this \emph{representation} of the objective function's gradient. Under certain conditions on the Fisher information matrix, we prove that at each iterate, the optimal value via MAN algorithms can be better than that of the multi-agent actor-critic (MAAC) algorithm using the standard gradients. To validate the usefulness of our proposed algorithms, we implement all the 3 MAN algorithms on a bi-lane traffic network to reduce the average network congestion. We observe an almost 25% reduction in the average congestion in 2 MAN algorithms; the average congestion in another MAN algorithm is on par with the MAAC algorithm. We also consider a generic 15 agent MARL; the performance of the MAN algorithms is again as good as the MAAC algorithm. We attribute the better performance of the MAN algorithms to their use of the above representation.

【15】 Large-Scale Learning with Fourier Features and Tensor Decompositions 标题：具有傅立叶特征和张量分解的大规模学习 链接：https://arxiv.org/abs/2109.01545

作者：Frederiek Wesel,Kim Batselier 机构：Delft Center for Systems and Control, Delft University of Technology 备注：9 pages, 6 figures 摘要：随机傅立叶特征提供了一种用核方法解决大规模机器学习问题的方法。其缓慢的蒙特卡罗收敛速度激发了对确定性傅里叶特征的研究，其近似误差随频率数呈指数下降。然而，由于它们的张量积结构，这些方法严重受到维数灾难的影响，限制了它们对二维或三维场景的适用性。在我们的方法中，我们通过利用确定性傅立叶特征的张量积结构克服了上述维数灾难，这使我们能够将模型参数表示为低秩张量分解。我们推导了一个单调收敛的块坐标下降算法，对于正则平方损失函数，其样本大小和输入的维数均为线性复杂度，允许使用确定性傅立叶特征学习分解形式的简约模型。我们通过数值实验证明了我们的低秩张量方法如何获得与相应的非参数模型相同的性能，始终优于随机傅立叶特征。 摘要：Random Fourier features provide a way to tackle large-scale machine learning problems with kernel methods. Their slow Monte Carlo convergence rate has motivated the research of deterministic Fourier features whose approximation error decreases exponentially with the number of frequencies. However, due to their tensor product structure these methods suffer heavily from the curse of dimensionality, limiting their applicability to two or three-dimensional scenarios. In our approach we overcome said curse of dimensionality by exploiting the tensor product structure of deterministic Fourier features, which enables us to represent the model parameters as a low-rank tensor decomposition. We derive a monotonically converging block coordinate descent algorithm with linear complexity in both the sample size and the dimensionality of the inputs for a regularized squared loss function, allowing to learn a parsimonious model in decomposed form using deterministic Fourier features. We demonstrate by means of numerical experiments how our low-rank tensor approach obtains the same performance of the corresponding nonparametric model, consistently outperforming random Fourier features.

【16】 LightAutoML: AutoML Solution for a Large Financial Services Ecosystem 标题：LightAutoML：面向大型金融服务生态系统的AutoML解决方案 链接：https://arxiv.org/abs/2109.01528

作者：Anton Vakhrushev,Alexander Ryzhkov,Maxim Savchenko,Dmitry Simakov,Rinchin Damdinov,Alexander Tuzhilin 机构：Sber AI Lab, Stern School of Business, NYU 摘要：我们介绍了一个名为LightAutoML的AutoML系统，该系统是为一家大型欧洲金融服务公司开发的，其生态系统满足了该生态系统对AutoML解决方案的一系列特殊要求。我们的框架在许多应用程序中进行了试点和部署，并在经验丰富的数据科学家的水平上执行，同时构建高质量的ML模型的速度明显快于这些数据科学家。我们还将我们的系统的性能与各种通用开源AutoML解决方案进行了比较，结果表明，对于大多数生态系统和OpenML问题，它的性能更好。我们还介绍了在开发AutoML系统并将其投入生产过程中所学到的经验教训。 摘要：We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of the experienced data scientists while building high-quality ML models significantly faster than these data scientists. We also compare the performance of our system with various general-purpose open source AutoML solutions and show that it performs better for most of the ecosystem and OpenML problems. We also present the lessons that we learned while developing the AutoML system and moving it into production.

【17】 Estimating Demand Flexibility Using Siamese LSTM Neural Networks 标题：基于暹罗LSTM神经网络的需求弹性估算 链接：https://arxiv.org/abs/2109.01258

作者：Guangchun Ruan,Daniel S. Kirschen,Haiwang Zhong,Qing Xia,Chongqing Kang 机构： Department of Electrical Engineering, Tsinghua University, Kirschen is with the Department of Electrical & Computer Engineer-ing, University of Washington 备注：Author copy of the manuscript submitted to IEEE Trans on Power Systems 摘要：在现代电力系统中，有机会通过动态价格激励消费者，探索需求的灵活性。在本文中，我们使用一种称为时变弹性的有效工具来量化需求灵活性，该工具的价值可能随价格和决策动态而变化。该工具对于评估需求响应潜力和系统可靠性特别有用。最近的经验证据表明，在研究需求灵活性时，存在一些异常特征，如响应延迟和价格飙升后弹性消失。现有的方法无法捕获这些复杂的特征，因为它们严重依赖于一些预定义（通常过于简化）的回归表达式。相反，本文提出了一种无模型方法来自动准确地导出最佳估计模式。我们进一步利用连体长短时记忆（LSTM）网络开发了一个两阶段估计过程。这里，一个LSTM网络对价格响应进行编码，而另一个网络估计时变弹性。在案例研究中，与最先进的方法相比，所提出的框架和模型得到了验证，以实现更高的总体估计精度和对各种异常特征的更好描述。 摘要：There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods.

机器翻译，仅供参考