统计学学术速递[12.22]

stat统计学，共计31篇

【1】 Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding 标题：不可测混杂因果推断的双有效/双锐度灵敏度分析 链接：https://arxiv.org/abs/2112.11449

作者：Jacob Dorn,Kevin Guo,Nathan Kallus 机构：Princeton University, Stanford University, Cornell University 摘要：在Tan（2006）的边际敏感性模型下，我们研究了在存在未观察到的混杂因素的情况下构建平均治疗效果的界限的问题。结合现有的涉及对抗倾向得分的特征和问题的新分布稳健特征，我们提出了这些界的新估计量，我们称之为“双有效/双锐”（DVDS）估计量。双锐度对应于这样一个事实，即即使两个干扰参数中的一个被错误指定，DVDS估计器也会一致地估计灵敏度模型所暗示的最紧可能（即锐度）的界限，并且在所有干扰参数适当一致时达到半参数效率。双重有效性是部分识别的一个全新属性：DVDS估计器即使在大多数讨厌的参数被错误指定的情况下，仍然提供有效的，但不尖锐的界限。事实上，即使在DVDS点估计不能渐近正态的情况下，标准Wald置信区间也可能仍然有效。在二元结果的情况下，DVDS估计器特别方便，并且在结果回归和倾向得分方面具有封闭形式的表达式。我们在模拟研究以及右心导管插入术的案例研究中展示了DVDS估计器。 摘要：We study the problem of constructing bounds on the average treatment effect in the presence of unobserved confounding under the marginal sensitivity model of Tan (2006). Combining an existing characterization involving adversarial propensity scores with a new distributionally robust characterization of the problem, we propose novel estimators of these bounds that we call "doubly-valid/doubly-sharp" (DVDS) estimators. Double sharpness corresponds to the fact that DVDS estimators consistently estimate the tightest possible (i.e., sharp) bounds implied by the sensitivity model even when one of two nuisance parameters is misspecified and achieve semiparametric efficiency when all nuisance parameters are suitably consistent. Double validity is an entirely new property for partial identification: DVDS estimators still provide valid, though not sharp, bounds even when most nuisance parameters are misspecified. In fact, even in cases when DVDS point estimates fail to be asymptotically normal, standard Wald confidence intervals may remain valid. In the case of binary outcomes, the DVDS estimators are particularly convenient and possesses a closed-form expression in terms of the outcome regression and propensity score. We demonstrate the DVDS estimators in a simulation study as well as a case study of right heart catheterization.

【2】 NN2Poly: A polynomial representation for deep feed-forward artificial neural networks 标题：NN2Poly：深度前馈人工神经网络的一种多项式表示 链接：https://arxiv.org/abs/2112.11397

作者：Pablo Morala,Jenny Alexandra Cifuentes,Rosa E. Lillo,Iñaki Ucar 机构：auc,m-Santander Big Data Institute, Universidad Carlos III de Madrid. Spain., Department of Statistics, Universidad Carlos III de Madrid. Spain., ICADE, Department of Quantitative Methods, Administration, Universidad Pontificia Comillas. Spain. 摘要：神经网络的可解释性及其潜在的理论行为仍然是一个开放的研究领域，即使在其实际应用取得巨大成功之后，特别是随着深度学习的出现。在这项工作中，提出了NN2Poly：一种理论方法，允许获得多项式，为已经训练的深层神经网络提供一种替代表示。这扩展了arXiv:2102.03865中提出的仅限于单隐层神经网络的先前想法，以在回归和分类任务中使用任意深度前馈神经网络。本文的目标是通过在每一层对激活函数使用泰勒展开，然后使用允许识别所需多项式系数的若干组合属性来实现的。讨论了实现该理论方法时的主要计算限制，并给出了NN2Poly工作所需的神经网络权重约束示例。最后，给出了一些仿真结果，得出结论，使用NN2Poly可以获得给定神经网络的表示，且获得的预测之间的误差较小。 摘要：Interpretability of neural networks and their underlying theoretical behaviour remain being an open field of study, even after the great success of their practical applications, particularly with the emergence of deep learning. In this work, NN2Poly is proposed: a theoretical approach that allows to obtain polynomials that provide an alternative representation of an already trained deep neural network. This extends the previous idea proposed in arXiv:2102.03865, which was limited to single hidden layer neural networks, to work with arbitrarily deep feed-forward neural networks in both regression and classification tasks. The objective of this paper is achieved by using a Taylor expansion on the activation function, at each layer, and then using several combinatorial properties that allow to identify the coefficients of the desired polynomials. The main computational limitations when implementing this theoretical method are discussed and it is presented an example of the constraints on the neural network weights that are necessary for NN2Poly to work. Finally, some simulations are presented were it is concluded that using NN2Poly it is possible to obtain a representation for the given neural network with low error between the obtained predictions.

【3】 Retrodictive Modelling of Modern Rugby Union: Extension of Bradley-Terry to Multiple Outcomes 标题：现代橄榄球联盟的追溯模型：布拉德利-特里对多重结果的扩展 链接：https://arxiv.org/abs/2112.11262

作者：Ian Hamilton,David Firth 机构： University of WarwickDavid Firth, University of WarwickAbstractFrequently in sporting competitions it is desirable to compare teams based on records of varyingschedule strength, a national schools tournament in England and Wales 摘要：在体育比赛中，经常需要根据不同赛程强度的记录对球队进行比较。已经为结果为胜利、平局或失败的运动制定了方法。本文将这些思想推广到任意有限多结果集。为现代橄榄球联盟提供了一种基于原则的动机，并提供了一种实施方案，在该方案中，如果在一定的得分差距内输球，并且在一定的尝试次数内得分，则会获得奖励积分。讨论了许多变量，包括每个变量所隐含的约束假设。该模型用于评估英格兰和威尔士全国学校锦标赛《每日邮报》的现行规则。 摘要：Frequently in sporting competitions it is desirable to compare teams based on records of varying schedule strength. Methods have been developed for sports where the result outcomes are win, draw, or loss. In this paper those ideas are extended to account for any finite multiple outcome result set. A principle-based motivation is supplied and an implementation presented for modern rugby union, where bonus points are awarded for losing within a certain score margin and for scoring a certain number of tries. A number of variants are discussed including the constraining assumptions that are implied by each. The model is applied to assess the current rules of the Daily Mail Trophy, a national schools tournament in England and Wales.

【4】 Group Lasso merger for sparse prediction with high-dimensional categorical data 标题：高维分类数据稀疏预测的成组套索合并 链接：https://arxiv.org/abs/2112.11114

作者：Szymon Nowakowski,Piotr Pokarowski,Wojciech Rejchel 机构： University of Warsaw 摘要：使用分类数据进行稀疏预测即使对于中等数量的变量也是一个挑战，因为编码一个类别或级别大致需要一个参数。组套索是一种众所周知的选择连续或分类变量的有效算法，但与选定因子相关的所有估计值通常不同，因此拟合模型可能不是稀疏的。为了使组套索解稀疏，我们建议合并所选因子的级别，如果其相应估计值之间的差异小于某个预定阈值。我们证明，在较弱的条件下，我们的算法，称为GLAMER组套索合并，恢复真实的，稀疏的线性或逻辑模型，即使在高维的情况下，即当一些参数大于一个学习样本大小。据我们所知，对于使用分类变量拟合稀疏模型的不同算法，已经多次证明了选择一致性，但我们的结果是高维场景中的第一个结果。数值实验表明，GLAMER具有令人满意的性能。 摘要：Sparse prediction with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection continuous or categorical variables, but all estimates related to a selected factor usually differ, so a fitted model may not be sparse. To make the Group Lasso solution sparse, we propose to merge levels of the selected factor, if a difference between its corresponding estimates is less than some predetermined threshold. We prove that under weak conditions our algorithm, called GLAMER for Group LAsso MERger, recovers the true, sparse linear or logistic model even for the high-dimensional scenario, that is when a number of parameters is greater than a learning sample size. To our knowledge, selection consistency has been proven many times for different algorithms fitting sparse models with categorical variables, but our result is the first for the high-dimensional scenario. Numerical experiments show the satisfactory performance of the GLAMER.

【5】 Risk bounds for aggregated shallow neural networks using Gaussian priors 标题：基于高斯先验的聚集浅层神经网络的风险界 链接：https://arxiv.org/abs/2112.11086

作者：Laura Tinsi,Arnak S. Dalalyan 机构：CREST, ENSAE Paris, Institut Polytechnique de Paris 摘要：分析神经网络的统计特性是统计学和机器学习的一个中心主题。然而，文献中的大多数结果集中在最小化训练误差的神经网络的性质上。本文的目的是考虑使用高斯先验的聚合神经网络。我们的方法的出发点是满足PAC贝叶斯不等式的任意集合。主要贡献是对PAC贝叶斯界中出现的估计误差进行精确的非渐近评估。我们的分析足够清晰，可以得出Sobolev光滑类上的极大极小估计率。 摘要：Analysing statistical properties of neural networks is a central topic in statistics and machine learning. However, most results in the literature focus on the properties of the neural network minimizing the training error. The goal of this paper is to consider aggregated neural networks using a Gaussian prior. The departure point of our approach is an arbitrary aggregate satisfying the PAC-Bayesian inequality. The main contribution is a precise nonasymptotic assessment of the estimation error appearing in the PAC-Bayes bound. Our analysis is sharp enough to lead to minimax rates of estimation over Sobolev smoothness classes.

【6】 Data blurring: sample splitting a single sample 标题：数据模糊：样本拆分单个样本 链接：https://arxiv.org/abs/2112.11079

作者：James Leiner,Boyan Duan,Larry Wasserman,Aaditya Ramdas 机构：Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 备注：45 pages, 31 figures 摘要：假设我们观察到一个随机向量$X$，它来自一个已知族中具有未知参数的分布$P$。我们提出以下问题：什么时候可以将$X$分为$f（X）$和$g（X）$两部分，这样两部分都不足以自行重建$X$，但两者一起可以完全回收$X$，并且$（f（X），g（X））$的联合分配是可处理的？例如，如果$X=（X_1，dots，X_n）$和$P$是产品分布，那么对于任何$m<n$，我们可以分割样本来定义$f（X）=（X_1，dots，X_m）$和$g（X）=（X{m+1}，dots，X_n）$。Rasines和Young（2021）提供了另一种方法来完成这项任务，方法是使用加性高斯噪声对$X$进行随机化，从而在有限样本中对高斯分布数据进行选择后推断，并渐进地对非高斯加性模型进行选择后推断。在本文中，我们提供了一种更通用的方法，通过借鉴贝叶斯推理的思想，在有限样本中实现这种分裂，从而产生一个（频率）解，该解可视为数据分裂的连续模拟。我们称我们的方法为数据模糊，作为数据分割、数据雕刻和p值掩蔽的替代方法。我们在一些典型应用中举例说明了该方法，例如用于趋势过滤的后选择推理和其他回归问题。 摘要：Suppose we observe a random vector $X$ from some distribution $P$ in a known family with unknown parameters. We ask the following question: when is it possible to split $X$ into two parts $f(X)$ and $g(X)$ such that neither part is sufficient to reconstruct $X$ by itself, but both together can recover $X$ fully, and the joint distribution of $(f(X),g(X))$ is tractable? As one example, if $X=(X_1,\dots,X_n)$ and $P$ is a product distribution, then for any $m

【7】 Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome 标题：多个预测因子与生存结果最大关联度的有效估计 链接：https://arxiv.org/abs/2112.10996

作者：Tzu-Jung Huang,Alex Luedtke,Ian W. McKeague 机构：Department of Statistics, University of Washington, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Department of Biostatistics, Columbia University 备注：102 pages, 7 figures, 4 tables 摘要：本文提出了一种新的选择后推断方法，用于筛选生存结果的高维预测因子。文献中已经对右删失结果数据的后选择推理进行了研究，但要使该方法在高维上既可靠又可计算扩展，还有许多工作要做。机器学习工具通常用于提供生存结果的{\it predicts}，但所选预测因子的估计效果存在确认偏差，除非将选择考虑在内。新方法涉及构建预测因子与生存结果之间线性关联的半参数有效估计量，用于构建检验统计量，以检测任何预测因子与结果之间是否存在关联。此外，类似于bagging的稳定化技术允许对产生的测试统计数据进行正常校准，这使得能够构建预测值和结果之间最大关联的置信区间，并且还大大降低了计算成本。理论结果表明，即使预测值的数量随着样本量的增加呈超线性增长，这种检验方法仍然有效，我们的模拟结果支持这种渐近保证表明了在中等样本量下的检验性能。新的方法被应用于识别与抗病毒药物效力相关的病毒基因表达模式的问题。 摘要：This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high-dimensions. Machine learning tools are commonly used to provide {\it predictions} of survival outcomes, but the estimated effect of a selected predictor suffers from confirmation bias unless the selection is taken into account. The new approach involves construction of semi-parametrically efficient estimators of the linear association between the predictors and the survival outcome, which are used to build a test statistic for detecting the presence of an association between any of the predictors and the outcome. Further, a stabilization technique reminiscent of bagging allows a normal calibration for the resulting test statistic, which enables the construction of confidence intervals for the maximal association between predictors and the outcome and also greatly reduces computational cost. Theoretical results show that this testing procedure is valid even when the number of predictors grows superpolynomially with sample size, and our simulations support that this asymptotic guarantee is indicative the performance of the test at moderate sample sizes. The new approach is applied to the problem of identifying patterns in viral gene expression associated with the potency of an antiviral drug.

【8】 Shared Frailty Models Based on Cancer Data 标题：基于癌症数据的共享脆弱性模型 链接：https://arxiv.org/abs/2112.10986

作者：Shikhar Tyagi,Arvind Pandey,David D Hanagal 机构：Department of Statistics, Central University of Rajasthan, Rajasthan, India, b Department of Statistics, Savitribai Phule Pune University, Pune, India. 备注：28 pages,3 figures,18 tables 摘要：传统的生存分析技术侧重于随着时间的推移故障的发生。在分析此类事件时，忽略数据样本中未观察到的相关协变量或异质性可能会导致不良后果。在这种情况下，脆弱性模型是研究未观察到的协变量影响的可行选择。在本文中，我们假设脆弱性与危险率成倍数关系。为了分析未观测到的异质性，我们提出了以广义威布尔（GW）为基线分布的逆高斯（IG）和广义林德利（GL）共享脆弱性模型。为了估计模型中的参数，提出了马尔可夫链蒙特卡罗技术的贝叶斯范式。模型选择标准已用于模型比较。使用共享脆弱性模型分析了三种不同的癌症数据集。为数据集提出了更好的模型。 摘要：Traditional survival analysis techniques focus on the occurrence of failures over the time. During analysis of such events, ignoring the related unobserved covariates or heterogeneity involved in data sample may leads us to adverse consequences. In this context, frailty models are the viable choice to investigate the effect of the unobserved covariates. In this article, we assume that frailty acts multiplicatively to hazard rate. We propose inverse Gaussian (IG) and generalized Lindley (GL) shared frailty models with generalized Weibull (GW) as baseline distribution in order to analyze the unobserved heterogeneity. To estimate the parameters in models, Bayesian paradigm of Markov Chain Monte Carlo technique has been proposed. Model selection criteria have been used for the comparison of models. Three different cancer data sets have been analyzed using the shared frailty models. Better models have been suggested for the data sets.

【9】 Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S 标题：美国区域性冠状病毒传播动力学的混合建模 链接：https://arxiv.org/abs/2112.10983

作者：Yue Bai,Abolfazl Safikhani,George Michailidis 摘要：全世界COVID-19的快速传播率使这种病毒成为2020年度最重要的挑战。不同地区（国家、州、县和市）的政府已经实施了许多缓解政策，以阻止这种病毒的传播。量化此类缓解策略对传播和恢复率的影响，以及预测每日新病例的发生率是两项关键任务。2019冠状病毒疾病的研究中，我们提出了一个混合建模框架，不仅考虑了这些策略，而且还利用空间和时间信息来表征COVID-19的发展模式。具体而言，开发了一个分段易感感染恢复（SIR）模型，而在该模型中，传播/恢复率发生显著变化的日期被定义为“断点”。利用融合套索和阈值分割的思想，设计了一种新的数据驱动的断点定位算法。为了提高预测能力并描述每日病例数之间的额外时间依赖性，该模型进一步与空间平滑协变量和向量自回归（VAR）模型耦合。该模型已应用于美国几个州和县，结果通过检测接近此类事件的断点，证实了“留在家里的命令”和一些州早期“重新开放”的效果。此外，该模型利用估计的时空协方差结构，对区域一级每日新病例的数量提供了令人满意的短期预测。他们也更好或等同于其他建议的模型在文献中，包括灵活的深度学习。最后，报告了所提方法在合成数据上的选定理论结果和实证性能，证明了所提方法的良好性能。 摘要：The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at different regional levels (country, state, county, and city) to stop the spread of this virus. Quantifying the effect of such mitigation strategies on the transmission and recovery rates, and predicting the rate of new daily cases are two crucial tasks. In this paper, we propose a hybrid modeling framework which not only accounts for such policies but also utilizes the spatial and temporal information to characterize the pattern of COVID-19 progression. Specifically, a piecewise susceptible-infected-recovered (SIR) model is developed while the dates at which the transmission/recover rates change significantly are defined as "break points" in this model. A novel and data-driven algorithm is designed to locate the break points using ideas from fused lasso and thresholding. In order to enhance the forecasting power and to describe additional temporal dependence among the daily number of cases, this model is further coupled with spatial smoothing covariates and vector auto-regressive (VAR) model. The proposed model is applied to several U.S. states and counties, and the results confirm the effect of "stay-at-home orders" and some states' early "re-openings" by detecting break points close to such events. Further, the model provided satisfactory short-term forecasts of the number of new daily cases at regional levels by utilizing the estimated spatio-temporal covariance structures. They were also better or on par with other proposed models in the literature, including flexible deep learning ones. Finally, selected theoretical results and empirical performance of the proposed methodology on synthetic data are reported which justify the good performance of the proposed method.

【10】 Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy 标题：树信息的贝叶斯多源域适应：使用口头尸检的跨人群概率死因分配 链接：https://arxiv.org/abs/2112.10978

作者：Zhenke Wu,Zehang Richard Li,Irena Chen,Mengbing Li 机构：Department of Biostatistics, University of Michigan, Ann Arbor, MI , USA, Michigan Institute for Data Science, Ann Arbor, MI , USA, Department of Statistics, University of California, Santa Cruz, CA , USA 备注：Main paper: 22 pages, 4 figures, 2 tables; Contains Supplementary Materials 摘要：确定发生在民事登记和人口动态统计系统之外的死亡原因（COD）具有挑战性。一种称为口头尸检（VA）的技术在实践中被广泛用于收集死亡信息。VA包括与死者亲属就死者在死亡期间的症状进行面谈，通常会产生多元二元反应。虽然已经设计了统计方法来估计研究人群的病因特异性死亡率分数（CSMFs），但VA继续扩展到新人群（或“领域”）需要在利用潜在相似性的同时识别领域间差异的方法。在本文中，我们提出了这样一种域自适应方法，该方法集成了由预先指定的有根加权树编码的外部域间相似性信息。给定原因，我们使用潜在类模型来描述响应的条件分布，这些响应可能因域而异。我们指定了一个逻辑斯蒂克-打破高斯扩散过程，然后沿着树将类混合权重与节点特定的尖峰和板优先级结合起来，以数据驱动的方式在域之间汇集信息。后验推理是通过可伸缩的变分贝叶斯算法进行的。仿真研究表明，该方法实现的域自适应改进了CSMF估计和个体COD分配。我们还使用验证数据集对该方法进行了说明和评估。本文最后讨论了其局限性和未来的发展方向。 摘要：Determining causes of deaths (COD) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this paper, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a pre-specified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. Posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation data set. The paper concludes with a discussion on limitations and future directions.

【11】 Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models 标题：微分均匀化：一种推导包含随机流行病模型的组合状态空间上马氏链的新方法 链接：https://arxiv.org/abs/2112.10971

作者：Kevin Rupp,Rudolf Schill,Jonas Süskind,Peter Georg,Maren Klever,Andreas Lösch,Lars Grasedyck,Tilo Wettig,Rainer Spang 机构：Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany, Department of Physics, University of Regensburg, Regensburg, Germany, Institut f¨ur Geometrie und Praktische Mathematik, RWTH Aachen University, Aachen, Germany 摘要：动机：我们考虑连续时间马尔可夫链描述动态系统的随机演变的过渡率矩阵$ Q$，这取决于参数$\θ$。计算$t$时状态的概率分布需要矩阵指数$\exp（tQ）$，从数据推断$\theta$需要其导数$\partial\exp\！（tQ）/\partial\theta$。当状态空间和$Q$的大小很大时，两者都很难计算。当状态空间由几个相互作用的离散变量的值的所有组合组成时，就会发生这种情况。通常甚至不可能存储$Q$。然而，当$Q$可以写为张量积之和时，通过均匀化方法计算$\exp（tQ）$是可行的，它不需要$Q$的显式存储。结果：这里我们提供了一个计算$\partial\exp\的类似算法！（tQ）/\partial\theta$，差异化均匀化方法。我们证明了传染病扩散的随机SIR模型的算法，我们证明了$Q$可以写成张量积的和。我们估计每月2019冠状病毒疾病在奥地利的感染和恢复率，并量化其不确定性的全面贝叶斯分析。可用性：实施和数据可在https://github.com/spang-lab/TenSIR. 摘要：Motivation: We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix $Q$ which depends on a parameter $\theta$. Computing the probability distribution over states at time $t$ requires the matrix exponential $\exp(tQ)$, and inferring $\theta$ from data requires its derivative $\partial\exp\!(tQ)/\partial\theta$. Both are challenging to compute when the state space and hence the size of $Q$ is huge. This can happen when the state space consists of all combinations of the values of several interacting discrete variables. Often it is even impossible to store $Q$. However, when $Q$ can be written as a sum of tensor products, computing $\exp(tQ)$ becomes feasible by the uniformization method, which does not require explicit storage of $Q$. Results: Here we provide an analogous algorithm for computing $\partial\exp\!(tQ)/\partial\theta$, the differentiated uniformization method. We demonstrate our algorithm for the stochastic SIR model of epidemic spread, for which we show that $Q$ can be written as a sum of tensor products. We estimate monthly infection and recovery rates during the first wave of the COVID-19 pandemic in Austria and quantify their uncertainty in a full Bayesian analysis. Availability: Implementation and data are available at https://github.com/spang-lab/TenSIR.

【12】 Improved Efficiency for Cross-Arm Comparisons via Platform Designs 标题：通过平台设计提高跨臂比较的效率 链接：https://arxiv.org/abs/2112.10967

作者：Tzu-Jung Huang,Alex Luedtke,the AMP Investigators Group 机构：Department of Statistics, University of Washington, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center 备注：60 pages, 7 figures, 4 tables 摘要：尽管平台试验因其灵活性和试验资源的合理使用而受到吹捧，但其统计效率尚不清楚。我们通过使用多个单独的两组试验来比较多种干预措施的相对有效性，通过对比其相对风险和对照来评估任意一对干预措施的相对有效性，从而填补了这一空白。在理论和数值研究中，我们证明，使用来自平台试验的数据推断这种对比比使用来自单独试验的数据具有相同或更好的精度，即使前者的参与者人数大大减少。这一益处归因于在同期随机化下干预措施之间共享控制，这是平台试验的一个关键特征。我们进一步提供了一种新的程序，用于确定给定干预相对于其他被评估干预中最有效的干预的非劣效性，其中该程序是适应性的，因为它不需要事先知道这些其他干预中哪种最有效。我们的数值研究表明，当数据来自一个平台试验而不是多个单独试验时，该测试程序可以获得更好的性能。我们的结果用两项预防艾滋病毒的单克隆抗体试验的数据加以说明。 摘要：Though platform trials have been touted for their flexibility and streamlined use of trial resources, their statistical efficiency is not well understood. We fill this gap by establishing their greater efficiency for comparing the relative efficacy of multiple interventions over using several separate, two-arm trials, where the relative efficacy of an arbitrary pair of interventions is evaluated by contrasting their relative risks as compared to control. In theoretical and numerical studies, we demonstrate that the inference of such a contrast using data from a platform trial enjoys identical or better precision than using data from separate trials, even when the former enrolls substantially fewer participants. This benefit is attributed to the sharing of controls among interventions under contemporaneous randomization, which is a key feature of platform trials. We further provide a novel procedure for establishing the non-inferiority of a given intervention relative to the most efficacious of the other interventions under evaluation, where this procedure is adaptive in the sense that it need not be \textit{a priori} known which of these other interventions is most efficacious. Our numerical studies show that this testing procedure can attain substantially better power when the data arise from a platform trial rather than multiple separate trials. Our results are illustrated using data from two monoclonal antibody trials for the prevention of HIV.

【13】 Short range vs long range dependence. An hyppothesis test based on Fractional Iterated Ornstein--Uhlenbeck processes 标题：短程VS长程依赖。基于分数次迭代Ornstein-Uhlenbeck过程的假设检验 链接：https://arxiv.org/abs/2112.10958

作者：Juan Kalemkerian,Andrés Sosa 机构：Andr´es Sosa, Universidad de la Rep´ublica, Facultad de Ciencias Econ´omicas, y Administraci´on 备注：22 pages, 5 figures 摘要：在这项工作中，我们基于分数阶迭代Ornstein-Uhlenbeck过程族，提出了一个新的假设检验来对比时间序列中的短记忆和长记忆。该系列包括短内存进程和长内存进程，并具有通过短内存进程近似长内存进程的能力。基于其参数估计的渐近结果，我们将给出测试并说明如何实现它。此外，我们还将与在短内存和长内存场景下广泛使用的其他测试进行比较。主要结论是，这种新的测试是在零假设下性能最好的测试，并且在某些备选方案中具有最大的威力。 摘要：In this work, which is based on the family of Fractional Iterated Ornstein Uhlenbeck processes, we propose a new hypothesis test to contrast short memory versus long memory in time series. This family includes short memory and long memory processes, and has the ability to approximate a long memory processes by a short memory processes. Based on the asymptotic results of the estimators of its parameters, we will present the test and show how it can be implemented. Also, we will show a comparison with other tests widely used under both short memory and long memory scenarios. The main conclusion is that this new test is the one with best performance under the null hypothesis, and has the maximum power in some alternatives.

【14】 Joint Learning of Linear Time-Invariant Dynamical Systems 标题：线性时不变动态系统的联合学习 链接：https://arxiv.org/abs/2112.10955

作者：Aditya Modi,Mohamad Kazem Shirani Faradonbeh,Ambuj Tewari,George Michailidis 机构：Computer Science and Engineering, University of Michigan & Microsoft Inc., Department of Statistics, University of Georgia, Department of Statistics, University of Michigan, Department of Statistics, University of Florida 摘要：学习线性时不变动力系统（LTIDS）的参数是当前感兴趣的问题。在许多应用中，人们感兴趣的是联合学习多个相关LTID的参数，这些参数迄今尚未被探索。为此，我们开发了一种联合估计，用于学习共享公共基矩阵的LTID的转移矩阵。此外，我们还建立了有限时间误差界，该误差界取决于潜在的样本大小、维数、任务数和转移矩阵的谱特性。结果是在温和的规律性假设下获得的，与单独学习每个系统相比，展示了跨LTID汇集信息的收益。我们还研究了错误指定转移矩阵的联合结构的影响，并表明在存在中度错误指定的情况下，已建立的结果是稳健的。 摘要：Learning the parameters of a linear time-invariant dynamical system (LTIDS) is a problem of current interest. In many applications, one is interested in jointly learning the parameters of multiple related LTIDS, which remains unexplored to date. To that end, we develop a joint estimator for learning the transition matrices of LTIDS that share common basis matrices. Further, we establish finite-time error bounds that depend on the underlying sample size, dimension, number of tasks, and spectral properties of the transition matrices. The results are obtained under mild regularity assumptions and showcase the gains from pooling information across LTIDS, in comparison to learning each system separately. We also study the impact of misspecifying the joint structure of the transition matrices and show that the established results are robust in the presence of moderate misspecifications.

【15】 An imprecise-probabilistic characterization of frequentist statistical inference 标题：频率统计推断的一种不精确概率刻画 链接：https://arxiv.org/abs/2112.10904

作者：Ryan Martin 备注：Supersedes arXiv:1707.00486. 51 pages, 11 figures. Comments welcome at this https URL 摘要：在统计学中的两个主要学派，即贝叶斯学派和经典/频率学派之间，一个主要区别是前者基于数学上严格的概率理论，而后者则不是。在本文中，我证明了后者是建立在一个不同但同样数学严格的不精确概率理论基础上的。具体地说，我证明了对于每一个具有错误率控制保证的合适的测试或置信程序，都存在一个辅音似然函数，其派生的测试或置信程序的效率同样高。除了其基本含义外，这一特征至少有两个重要的实际后果：第一，它简化了p值和信心区域的解释，从而为改善教育和科学交流创造了机会；第二，主要结果的建设性证明导致了在挑战推理问题中新的和改进的方法的策略。 摘要：Between the two dominant schools of thought in statistics, namely, Bayesian and classical/frequentist, a main difference is that the former is grounded in the mathematically rigorous theory of probability while the latter is not. In this paper, I show that the latter is grounded in a different but equally mathematically rigorous theory of imprecise probability. Specifically, I show that for every suitable testing or confidence procedure with error rate control guarantees, there exists a consonant plausibility function whose derived testing or confidence procedure is no less efficient. Beyond its foundational implications, this characterization has at least two important practical consequences: first, it simplifies the interpretation of p-values and confidence regions, thus creating opportunities for improved education and scientific communication; second, the constructive proof of the main results leads to a strategy for new and improved methods in challenging inference problems.

【16】 BOP2-DC: Bayesian optimal phase II designs with dual-criterion decision making 标题：BOP2-DC：带双准则决策的贝叶斯最优第二阶段设计 链接：https://arxiv.org/abs/2112.10880

作者：Yujie Zhao,Daniel Li,Rong Liu,Ying Yuan 机构： Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas , USA, Bristol Myers Squibb, Berkeley Heights, New Jersey , USA 摘要：传统的第二阶段试验设计范式是基于假设检验框架做出决定。然而，仅仅统计意义本身可能不足以证明该药物的临床疗效足以保证III期临床试验的验证性。我们提出了具有双标准决策的贝叶斯最优II期试验设计（BOP2-DC），它将统计学意义和临床相关性结合到决策中。基于治疗效果达到较低参考值（统计显著性）和临床意义值（临床显著性）的后验概率，BOP2-DC允许通过/考虑/不通过决策，而不是二元通过/不通过决策，当治疗有效时，它被优化为使go决策的概率最大化；当治疗无效时，它被优化为使样本量最小化。BOP2-DC具有高度灵活性，在单臂和随机试验中可容纳各种类型的终点，包括二元终点、连续终点、事件发生时间终点、多个终点和共同主要终点。仿真研究表明，BOP2-DC设计产生了理想的工作特性。实现BOP2-DC的软件可在\url{www.trialdesign.org}免费获得。 摘要：The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision, and it is optimized to maximize the probability of a go decision when the treatment is effective or minimize the sample size when the treatment is futile. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and co-primary endpoints, in single-arm and randomized trials. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at \url{www.trialdesign.org}.

【17】 Third-Order Moment Varieties of Linear Non-Gaussian Graphical Models 标题：线性非高斯图形模型的三阶矩变量 链接：https://arxiv.org/abs/2112.10875

作者：Carlos Améndola,Mathias Drton,Alexandros Grosdos,Roser Homs,Elina Robeva 机构：Department of Mathematics, Technical University of Munich, Germany, Department of Mathematics, University of British Columbia, Canada 备注：29 pages 摘要：本文从代数统计的角度研究了线性非高斯图形模型。这些是非循环因果模型，其中每个变量是其直接原因和独立噪声的线性组合。通过对应模型中所有随机向量的二阶和三阶矩集，可以唯一地识别潜在的有向因果图。我们的重点是找到给定图的这些矩之间的代数关系。我们证明了当图是一个多树时，这些关系形成了一个复曲面理想。我们构造了与图中的2-trek和3-trek相关联的显式trek矩阵。它们的条目是协方差和三阶矩，它们的2-子项从理论上定义了我们的模型集。此外，我们还证明了它们的2-子代也产生了模型的消失理想。最后，我们描述了三阶矩的多面体和隐变量模型的理想。 摘要：In this paper we study linear non-Gaussian graphical models from the perspective of algebraic statistics. These are acyclic causal models in which each variable is a linear combination of its direct causes and independent noise. The underlying directed causal graph can be identified uniquely via the set of second and third order moments of all random vectors that lie in the corresponding model. Our focus is on finding the algebraic relations among these moments for a given graph. We show that when the graph is a polytree these relations form a toric ideal. We construct explicit trek-matrices associated to 2-treks and 3-treks in the graph. Their entries are covariances and third order moments and their 2-minors define our model set-theoretically. Furthermore, we prove that their 2-minors also generate the vanishing ideal of the model. Finally, we describe the polytopes of third order moments and the ideals for models with hidden variables.

【18】 Detection of causality in time series using extreme values 标题：基于极值的时间序列因果关系检测 链接：https://arxiv.org/abs/2112.10858

作者：Juraj Bodik,Milan Paluš,Zbyněk Pawlas 机构：Institute of Computer Science, The Czech Academy of Sciences, Department of Probability and Mathematical Statistics, Charles, University, Prague, Czech Republic. 备注：42 pages, 8 figures 摘要：考虑具有重尾边分布的两个平稳时间序列。我们想要检测它们是否有因果关系，也就是说，如果其中一个的变化导致另一个的变化。如果因果机制只在极端情况下表现出来，通常的因果关系检测方法就不太合适。在这篇文章中，我们提出了新的见解，可以帮助在这种非传统情况下进行因果检测。我们定义了时间序列的因果尾系数，在某些假设下，该系数能够正确地检测不同时间序列之间的非对称因果关系。其优点是，即使存在非线性关系和共同祖先，这种方法也能工作。此外，我们还提到了我们的方法如何帮助检测两个时间序列之间的时间延迟。我们描述了它的一些特性，并展示了它在一些模拟中的表现。最后，我们在空间天气和水文气象数据集上展示了该方法在实践中的工作原理。 摘要：Consider two stationary time series with heavy-tailed marginal distributions. We want to detect whether they have a causal relation, that is, if a change in one of them causes a change in the other. Usual methods for causality detection are not well suited if the causal mechanisms only manifest themselves in extremes. In this article, we propose new insight that can help with causal detection in such a non-traditional case. We define the so-called causal tail coefficient for time series, which, under some assumptions, correctly detects the asymmetrical causal relations between different time series. The advantage is that this method works even if nonlinear relations and common ancestors are present. Moreover, we mention how our method can help detect a time delay between the two time series. We describe some of its properties, and show how it performs on some simulations. Finally, we show on a space-weather and hydro-meteorological data sets how this method works in practice.

【19】 Unique Distributions Under Non-IID Assumption 标题：非IID假设下的唯一分布 链接：https://arxiv.org/abs/2112.10836

作者：K. P. Chowdhury 机构：Johns Hopkins Universitydukechowdhury, I would also like to thank the University of California 摘要：讨论了强收敛M-估计的应用。鉴于分布在各学科中的普遍性，阐述了在物理、生物医学和社会科学中的多种应用。在一个特定的实现中，实现了独特的实用程序。最后，强调了结果和发现对模型拟合、推理和预测的重要性，以使其在各学科中具有广泛的适用性。 摘要：Applications of Strongly Convergent M-Estimators are discussed. Given the ubiquity of distributions across the sciences, multiple applications in the Physical, Biomedical and Social Sciences are elaborated. In one particular implementation unique utilities are attained. Finally, the importance of the results and findings to model fit, inference and prediction are highlighted for broad applicability across the sciences.

【20】 Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces 标题：基于对抗性机器学习和可重构智能表面的隐蔽通信 链接：https://arxiv.org/abs/2112.11414

作者：Brian Kim,Tugba Erpek,Yalin E. Sagduyu,Sennur Ulukus 机构：Department of Electrical and Computer Engineering, University of Maryland, College Park, MD , USA, Intelligent Automation, Inc., Rockville, MD , USA 摘要：通过为软件定义的无线系统从大型天线移动到天线表面，可重构智能表面（RIS）依靠单元单元阵列来控制信号的散射和反射剖面，减轻传播损耗和多径衰减，从而提高覆盖范围和频谱效率。本文考虑了RIS存在时的隐蔽通信。虽然RIS促进了正在进行的传输，但预期的接收者和窃听者都各自尝试使用自己的深层神经网络（DNN）分类器检测该传输。RIS交互向量是通过平衡两个（潜在冲突的）目标来设计的，这两个目标是将发送信号聚焦到接收器，并使发送信号远离窃听者。为了加强隐蔽通信，在发射机的信号中加入对抗性干扰，以欺骗窃听者的分类器，同时保持对接收机的低影响。来自不同网络拓扑的结果表明，对抗性干扰和RIS交互向量可以联合设计，以有效地提高接收器处的信号检测精度，同时降低窃听者处的检测精度，从而实现隐蔽通信。 摘要：By moving from massive antennas to antenna surfaces for software-defined wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals, mitigating the propagation loss and multipath attenuation, and thereby improving the coverage and spectral efficiency. In this paper, covert communication is considered in the presence of the RIS. While there is an ongoing transmission boosted by the RIS, both the intended receiver and an eavesdropper individually try to detect this transmission using their own deep neural network (DNN) classifiers. The RIS interaction vector is designed by balancing two (potentially conflicting) objectives of focusing the transmitted signal to the receiver and keeping the transmitted signal away from the eavesdropper. To boost covert communications, adversarial perturbations are added to signals at the transmitter to fool the eavesdropper's classifier while keeping the effect on the receiver low. Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.

【21】 Toward Explainable AI for Regression Models 标题：回归模型的可解释人工智能 链接：https://arxiv.org/abs/2112.11407

作者：Simon Letzgus,Patrick Wagner,Jonas Lederer,Wojciech Samek,Klaus-Robert Müller,Gregoire Montavon 机构：Gr´egoire Montavon∗, Machine Learning Group, Technische Universit¨at Berlin, Berlin, Germany, Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany 备注：17 pages, 10 figures, preprint 摘要：除了机器学习（ML）模型令人印象深刻的预测能力外，最近出现了解释方法，可以解释复杂的非线性学习模型，如深度神经网络。获得更好的理解尤其重要，例如对于安全关键的ML应用程序或医疗诊断等。虽然此类可解释AI（XAI）技术在分类器中已经非常流行，但到目前为止，很少有人关注回归模型（XAIR）的XAI。在这篇综述中，我们阐明了XAI在回归和分类任务中的基本概念差异，为XAIR建立了新的理论见解和分析，提供了XAIR在真实实际回归问题上的演示，最后讨论了该领域仍然存在的挑战。 摘要：In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks. Gaining a better understanding is especially important e.g. for safety-critical ML applications or medical diagnostics etc. While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally discuss the challenges remaining for the field.

【22】 Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility Across Independent System Operators in the United States 标题：美国独立系统运营商可变可再生能源普及率对电价及其波动性的影响 链接：https://arxiv.org/abs/2112.11338

作者：Olukunle O. Owolabi,Toryn L. J. Schafer,Georgia E. Smits,Sanhita Sengupta,Sean E. Ryan,Lan Wang,David S. Matteson,Mila Getmansky Sherman,Deborah A. Sunter 机构：Department of Mechanical Engineering, Tufts University, USA, Department of Statistics and Data Science, Cornell University, USA, School of Statistics, University of Minnesota, USA, Department of Management Science, University of Miami, USA 摘要：随着风能和太阳能——可变可再生能源（VRE）形式的普及，美国电网经历了重大变革。尽管VRE对脱碳有好处，但它在区域电力市场中引发了一些不必要的影响，引起了一些争议。在本研究中，我们基于美国六家独立系统运营商的每小时、实时、历史数据，使用分位数和斜t分布回归分析了VRE渗透对系统电价和价格波动的作用。在修正了时间效应后，我们观察到价格下降，对价格波动产生非线性影响，从而导致VRE渗透率增加。这些结果与现代投资组合理论一致，在现代投资组合理论中，不同的波动性资产可能导致更稳定、风险更低的投资组合。 摘要：The U.S. electrical grid has undergone substantial transformation with increased penetration of wind and solar -- forms of variable renewable energy (VRE). Despite the benefits of VRE for decarbonization, it has garnered some controversy for inducing unwanted effects in regional electricity markets. In this study, we examine the role of VRE penetration on the system electricity price and price volatility based on hourly, real-time, historical data from six Independent System Operators in the U.S. using quantile and skew t-distribution regressions. After correcting for temporal effects, we observe a decrease in price, with non-linear effects on price volatility, for an increase in VRE penetration. These results are consistent with the modern portfolio theory where diverse volatile assets may lead to more stable and less risky portfolios.

【23】 Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach 标题：一种基于精度的状态空间混频无功有效估计方法 链接：https://arxiv.org/abs/2112.11315

作者：Joshua C. C. Chan,Aubrey Poon,Dan Zhu 机构：Purdue University, Orebro University, Monash University 摘要：状态空间混合频率向量自回归现在广泛应用于临近预报。尽管这些模型很受欢迎，但估计这些模型可能需要大量计算，特别是对于具有随机波动性的大型系统。为了应对计算挑战，我们提出了两种新的基于精度的采样器，以提取这些模型中低频变量的缺失观测值，这是基于状态空间模型的频带和稀疏矩阵算法的最新进展。我们通过仿真研究表明，与基于标准卡尔曼滤波的方法相比，所提出的方法在数值精度和计算效率上更高。我们展示了该方法如何应用于两个实证宏观经济应用：估计月度产出缺口和研究GDP对月度货币政策冲击的响应。这两个实证应用的结果突出了在宏观经济模型中纳入高频指标的重要性。 摘要：State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computational challenges, we propose two novel precision-based samplers to draw the missing observations of the low-frequency variables in these models, building on recent advances in the band and sparse matrix algorithms for state-space models. We show via a simulation study that the proposed methods are more numerically accurate and computationally efficient compared to standard Kalman-filter based methods. We demonstrate how the proposed method can be applied in two empirical macroeconomic applications: estimating the monthly output gap and studying the response of GDP to a monetary policy shock at the monthly frequency. Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.

【24】 Preserving gauge invariance in neural networks 标题：在神经网络中保持规范不变性 链接：https://arxiv.org/abs/2112.11239

作者：Matteo Favoni,Andreas Ipp,David I. Müller,Daniel Schuh 机构：Institute for Theoretical Physics, TU Wien, Wiedner Hauptstr. ,-, Vienna, Austria, Speaker and corresponding author 备注：8 pages, 3 figures, proceedings for vConf 2021 摘要：在这些过程中，我们提出了格点规范等变卷积神经网络（L-CNN），它能够处理来自格点规范理论模拟的数据，同时精确地保持规范对称性。我们回顾了体系结构的各个方面，并展示了L-CNN如何在格上表示一大类规范不变和等变函数。我们使用非线性回归问题比较了L-CNN和非等变网络的性能，并证明了非等变模型的规范不变性是如何被打破的。 摘要：In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly preserving gauge symmetry. We review aspects of the architecture and show how L-CNNs can represent a large class of gauge invariant and equivariant functions on the lattice. We compare the performance of L-CNNs and non-equivariant networks using a non-linear regression problem and demonstrate how gauge invariance is broken for non-equivariant models.

【25】 How are cities pledging net zero? A computational approach to analyzing subnational climate strategies 标题：城市是如何承诺净零的呢？一种分析国家以下气候战略的计算方法 链接：https://arxiv.org/abs/2112.11207

作者：Siddharth Sachdeva,Angel Hsu,Ian French,Elwin Lim 机构： Data-Driven EnviroLab, University of North Carolina-Chapel Hill, S. Columbia Street, Chapel Hill, NC , Department University of North Carolina-Chapel Hill, S. Columbia Street, Chapel Hill, NC, Yale-NUS College, College Ave W, Singapore 备注：14 pages, 6 figures, submitted to nature urban sustainability 摘要：城市已经成为气候变化的主要参与者，并且越来越多地制定旨在实现净零排放的目标。国家以下各级政府“竞相实现零排放”并阐明自己的气候缓解计划的迅速扩散，需要进行更仔细的检查，以了解这些行为者打算如何实现这些目标。然而，城市气候政策文件的分散性、不完整性和异质性使得其系统分析具有挑战性。我们使用基于机器学习的自然语言处理（NLP）技术分析了318份气候行动文件，这些文件来自承诺实现净零目标的城市或加入了一项跨国气候倡议。我们使用这些方法来实现两个主要目标：1）确定预测“雄心勃勃”净零排放目标的文本模式，其中我们将雄心勃勃的目标定义为包含国家以下各级政府经济范围排放量的目标；2）进行部门分析，以确定气候行动主题（即土地利用、工业、建筑等）的模式和权衡。我们发现，定义了雄心勃勃的气候行动的城市往往在其计划中强调量化指标和特定的高排放部门，并提到治理和公民参与。城市在其计划中主要强调与能源有关的行动，特别是在建筑、运输和供暖部门，但往往以牺牲其他部门为代价，包括土地使用和气候影响。本文提出的方法为分析气候行动计划提供了一种可复制、可扩展的方法，也是促进跨城市学习的第一步。 摘要：Cities have become primary actors on climate change and are increasingly setting goals aimed at net-zero emissions. The rapid proliferation of subnational governments "racing to zero" emissions and articulating their own climate mitigation plans warrants closer examination to understand how these actors intend to meet these goals. The scattered, incomplete and heterogeneous nature of city climate policy documents, however, has made their systemic analysis challenging. We analyze 318 climate action documents from cities that have pledged net-zero targets or joined a transnational climate initiative with this goal using machine learning-based natural language processing (NLP) techniques. We use these approaches to accomplish two primary goals: 1) determine text patterns that predict "ambitious" net-zero targets, where we define an ambitious target as one that encompasses a subnational government's economy-wide emissions; and 2) perform a sectoral analysis to identify patterns and trade-offs in climate action themes (i.e., land-use, industry, buildings, etc.). We find that cities that have defined ambitious climate actions tend to emphasize quantitative metrics and specific high-emitting sectors in their plans, supported by mentions of governance and citizen participation. Cities predominantly emphasize energy-related actions in their plans, particularly in the buildings, transport and heating sectors, but often at the expense of other sectors, including land-use and climate impacts. The method presented in this paper provides a replicable, scalable approach to analyzing climate action plans and a first step towards facilitating cross-city learning.

【26】 Manifold learning via quantum dynamics 标题：基于量子动力学的流形学习 链接：https://arxiv.org/abs/2112.11161

作者：Akshat Kumar,Mohan Sarovar 机构：)Department of Mathematics, Clarkson University, Potsdam, NY , USA, )Instituto de Telecomunica¸c˜oes, Lisbon, Portugal, )Sandia National Laboratories, Livermore, California , USA 备注：This is a companion paper to "On a quantum-classical correspondence: from graphs to manifolds" arXiv:2112.10748 摘要：我们介绍了一种在采样流形上计算测地线的算法，该算法依赖于在采样数据的图嵌入上模拟量子动力学。我们的方法利用了半经典分析和量子经典对应中的经典结果，并为学习数据集采样的流形以及随后高维数据集的非线性降维技术奠定了基础。我们说明了新的算法，从模型歧管采样数据，并通过聚类演示基于COVID-19流动性数据。最后，我们的方法揭示了数据采样和量化提供的离散化之间有趣的联系。 摘要：We introduce an algorithm for computing geodesics on sampled manifolds that relies on simulation of quantum dynamics on a graph embedding of the sampled data. Our approach exploits classic results in semiclassical analysis and the quantum-classical correspondence, and forms a basis for techniques to learn the manifold from which a dataset is sampled, and subsequently for nonlinear dimensionality reduction of high-dimensional datasets. We illustrate the new algorithm with data sampled from model manifolds and also by a clustering demonstration based on COVID-19 mobility data. Finally, our method reveals interesting connections between the discretization provided by data sampling and quantization.

【27】 Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital 标题：用Shapley加法解释机器学习模型医院实际数据的解释与应用 链接：https://arxiv.org/abs/2112.11071

作者：Yasunobu Nohara,Koutarou Matsumoto,Hidehisa Soejima,Naoki Nakashima 机构：Nakashima, Kumamoto University, Kumamoto, JAPAN, Kurume University, Fukuoka, JAPAN, Saiseikai Kumamoto Hospital, Kumamoto, JAPAN, Kyushu University Hospital, Fukuoka, JAPAN 备注：Computer Methods and Programs in Biomedicine, Vol. 214, Article 106584 摘要：在决策过程中使用机器学习技术时，模型的可解释性非常重要。在本论文中，我们采用Shapley加法解释（SHAP），该解释基于多个利益相关者之间的公平利润分配，取决于他们的贡献，用于解释使用医院数据的梯度提升决策树模型。为了更好的解释性，我们提出了以下两种新技术：（1）使用SHAP的新特征重要性度量；（2）称为特征打包的技术，该技术将多个相似特征打包为一个分组特征，以便在不重建模型的情况下更容易理解模型。然后，我们比较了SHAP框架和现有方法之间的解释结果。此外，我们利用我们的医院数据和建议的技术，展示了A/G比率如何作为脑梗死的一个重要预后因素。 摘要：When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit allocation among many stakeholders depending on their contribution, for interpreting a gradient-boosting decision tree model using hospital data. For better interpretability, we propose two novel techniques as follows: (1) a new metric of feature importance using SHAP and (2) a technique termed feature packing, which packs multiple similar features into one grouped feature to allow an easier understanding of the model without reconstruction of the model. We then compared the explanation results between the SHAP framework and existing methods. In addition, we showed how the A/G ratio works as an important prognostic factor for cerebral infarction using our hospital data and proposed techniques.

【28】 Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition 标题：扩展-压缩-激励融合网络在老年人活动识别中的应用 链接：https://arxiv.org/abs/2112.10992

作者：Xiangbo Shu,Jiawen Yang,Rui Yan,Yan Song 机构： Song are with the School of Computer Sci-ence and Engineering, Nanjing University of Science and Technology 摘要：由于老年人活动中存在个体行为和人-物交互作用，老年人活动识别是一项具有挑战性的任务。因此，我们试图通过仔细融合多模态特征，从RGB视频和骨架序列中有效地聚集动作和交互的鉴别信息。近年来，一些非线性多模态融合方法被提出，它们利用了从挤压和激励网络（SENet）扩展而来的非线性注意机制。受此启发，我们提出了一种新的扩展-挤压激励融合网络（ESE-FN）来有效地解决老年人活动识别问题，该网络学习模态和通道扩展-挤压激励（ESE）注意事项，以便在模态和通道方式中仔细融合多模态特征。此外，我们设计了一种新的多模态损失（ML），通过增加单个模态上的最小预测损失与融合模态上的预测损失之间的差值惩罚，来保持单模态特征与融合多模态特征之间的一致性。最后，我们在一个最大规模的老年人活动数据集，即ETRI-Activity3D（包括110000多个视频和50多个类别）上进行了实验，以证明所提出的ESE-FN与最先进的方法相比达到了最佳精度。此外，更广泛的实验结果表明，所提出的ESE-FN在正常动作识别任务方面也与其他方法相当。 摘要：This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities. Thus, we attempt to effectively aggregate the discriminative information of actions and interactions from both RGB videos and skeleton sequences by attentively fusing multi-modal features. Recently, some nonlinear multi-modal fusion approaches are proposed by utilizing nonlinear attention mechanism that is extended from Squeeze-and-Excitation Networks (SENet). Inspired by this, we propose a novel Expansion-Squeeze-Excitation Fusion Network (ESE-FN) to effectively address the problem of elderly activity recognition, which learns modal and channel-wise Expansion-Squeeze-Excitation (ESE) attentions for attentively fusing the multi-modal features in the modal and channel-wise ways. Furthermore, we design a new Multi-modal Loss (ML) to keep the consistency between the single-modal features and the fused multi-modal features by adding the penalty of difference between the minimum prediction losses on single modalities and the prediction loss on the fused modality. Finally, we conduct experiments on a largest-scale elderly activity dataset, i.e., ETRI-Activity3D (including 110,000+ videos, and 50+ categories), to demonstrate that the proposed ESE-FN achieves the best accuracy compared with the state-of-the-art methods. In addition, more extensive experimental results show that the proposed ESE-FN is also comparable to the other methods in terms of normal action recognition task.

【29】 Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design 标题：基于强化学习的序贯批抽样贝叶斯最优试验设计 链接：https://arxiv.org/abs/2112.10944

作者：Yonatan Ashenafi,Piyush Pandita,Sayan Ghosh 机构：University of Alberta, Edmonton, T,G ,R, AB, Canada, General Electric Research, Niskayuna, New York, United States 摘要：使用复杂的数学方法建模的工程问题，或以昂贵的测试或实验为特征的工程问题，都被有限的预算或有限的计算资源所困扰。此外，行业中的实际情况会根据物流和偏好对实验进行的方式施加限制。例如，材料供应可能只允许在一次试验中进行少量试验，或者在计算模型的情况下，可能会面临基于共享计算资源的大量等待时间。在这种情况下，人们通常会以一种允许最大化自己的知识状态，同时满足上述实际约束条件的方式进行实验。顺序实验设计（SDOE）是一套流行的方法，近年来在不同的工程和实际问题上取得了有希望的结果。利用贝叶斯形式主义的一种常见策略是贝叶斯SDOE，它通常最适用于提前一步或短视场景，即在一系列实验的每一步选择单个实验。在这项工作中，我们的目标是扩展SDOE策略，在一批输入中查询实验或计算机代码。为此，我们利用基于深度强化学习（RL）的策略梯度方法，提出批量查询，这些查询的选择考虑了手头的全部预算。该算法保留了SDOE固有的顺序性，同时结合了基于深度RL领域任务的奖励元素。所提出的方法的一个独特功能是，一旦对其进行训练，它就能够应用于多个任务，例如优化功能。我们在一个综合问题和一个具有挑战性的高维工程问题上演示了该算法的性能。 摘要：Engineering problems that are modeled using sophisticated mathematical methods or are characterized by expensive-to-conduct tests or experiments, are encumbered with limited budget or finite computational resources. Moreover, practical scenarios in the industry, impose restrictions, based on logistics and preference, on the manner in which the experiments can be conducted. For example, material supply may enable only a handful of experiments in a single-shot or in the case of computational models one may face significant wait-time based on shared computational resources. In such scenarios, one usually resorts to performing experiments in a manner that allows for maximizing one's state-of-knowledge while satisfying the above mentioned practical constraints. Sequential design of experiments (SDOE) is a popular suite of methods, that has yielded promising results in recent years across different engineering and practical problems. A common strategy, that leverages Bayesian formalism is the Bayesian SDOE, which usually works best in the one-step-ahead or myopic scenario of selecting a single experiment at each step of a sequence of experiments. In this work, we aim to extend the SDOE strategy, to query the experiment or computer code at a batch of inputs. To this end, we leverage deep reinforcement learning (RL) based policy gradient methods, to propose batches of queries that are selected taking into account entire budget in hand. The algorithm retains the sequential nature, inherent in the SDOE, while incorporating elements of reward based on task from the domain of deep RL. A unique capability of the proposed methodology is its ability to be applied to multiple tasks, for example optimization of a function, once its trained. We demonstrate the performance of the proposed algorithm on a synthetic problem, and a challenging high-dimensional engineering problem.

【30】 Nearly Optimal Policy Optimization with Stable at Any Time Guarantee 标题：具有随时稳定保证的近最优策略优化 链接：https://arxiv.org/abs/2112.10935

作者：Tianhao Wu,Yunchang Yang,Han Zhong,Liwei Wang,Simon S. Du,Jiantao Jiao 机构：University of California, Berkeley, Center for Data Science, Peking University, University of Washington, Key Laboratory of Machine Perception (MOE), School of EECS, Peking University 摘要：策略优化方法是强化学习（RL）算法中应用最广泛的一类。然而，对这些方法的理论理解仍然不够。即使在幕式（时间不均匀）表格环境中，基于政策的方法在{Shani2020Optimized}中的最新理论结果也只有$\tilde{O}（\sqrt{S^2AH^4K}）$，其中$$S$是状态数，$A$是行动数，$H$是地平线，$K$是幕数，与信息论下限$\tilde{\Omega}（\sqrt{SAH^3K}）$相比，存在$\sqrt{SH}$差距。为了弥补这一差距，我们提出了一种新的基于参考的策略优化算法，该算法具有随时稳定的保证（\algnameacro），具有“随时稳定”的特性。我们证明了我们的算法实现了$\tilde{O}（\sqrt{SAH^3K}+\sqrt{AH^4}）$遗憾。当$S>H$时，忽略对数因子，我们的算法是极小极大最优的。据我们所知，RPO-SAT是第一个计算效率高、近似最大极小最优的基于策略的表格RL算法。 摘要：Policy optimization methods are one of the most widely used classes of Reinforcement Learning (RL) algorithms. However, theoretical understanding of these methods remains insufficient. Even in the episodic (time-inhomogeneous) tabular setting, the state-of-the-art theoretical result of policy-based method in \citet{shani2020optimistic} is only $\tilde{O}(\sqrt{S^2AH^4K})$ where $S$ is the number of states, $A$ is the number of actions, $H$ is the horizon, and $K$ is the number of episodes, and there is a $\sqrt{SH}$ gap compared with the information theoretic lower bound $\tilde{\Omega}(\sqrt{SAH^3K})$. To bridge such a gap, we propose a novel algorithm Reference-based Policy Optimization with Stable at Any Time guarantee (\algnameacro), which features the property "Stable at Any Time". We prove that our algorithm achieves $\tilde{O}(\sqrt{SAH^3K} + \sqrt{AH^4})$ regret. When $S > H$, our algorithm is minimax optimal when ignoring logarithmic factors. To our best knowledge, RPO-SAT is the first computationally efficient, nearly minimax optimal policy-based algorithm for tabular RL.

【31】 The effective noise of Stochastic Gradient Descent 标题：随机梯度下降的有效噪声 链接：https://arxiv.org/abs/2112.10852

作者：Francesca Mignacco,Pierfrancesco Urbani 机构：Université Paris-Saclay, CNRS, CEA, Institut de physique théorique, Gif-sur-Yvette, France. 备注：7 pages + appendix, 5 figures 摘要：随机梯度下降法（SGD）是深度学习技术的主要算法。在训练阶段的每一步，从训练数据集中抽取一小批样本，并根据该特定样本子集的性能调整神经网络的权重。小批量抽样过程在梯度下降过程中引入了随机动力学，并带有非平凡的状态相关噪声。在一个典型的神经网络模型中，我们描述了SGD的随机性和最近引入的变体，持久性SGD。在欠参数化区域，当最终训练误差为正时，SGD动力学达到平稳状态，我们根据波动耗散定理定义了有效温度，该定理由动力学平均场理论计算得出。我们使用有效温度来量化SGD噪声的大小，作为问题参数的函数。在训练误差为零的过参数化区域，我们通过计算具有相同初始化和两种不同SGD噪声实现的系统的两个副本之间的平均距离来测量SGD的噪声幅度。我们发现，作为问题参数的函数，这两种噪声度量的行为相似。此外，我们观察到，噪声较大的算法会导致相应约束满足问题的决策边界变宽。 摘要：Stochastic Gradient Descent (SGD) is the workhorse algorithm of deep learning technology. At each step of the training phase, a mini batch of samples is drawn from the training dataset and the weights of the neural network are adjusted according to the performance on this specific subset of examples. The mini-batch sampling procedure introduces a stochastic dynamics to the gradient descent, with a non-trivial state-dependent noise. We characterize the stochasticity of SGD and a recently-introduced variant, persistent SGD, in a prototypical neural network model. In the under-parametrized regime, where the final training error is positive, the SGD dynamics reaches a stationary state and we define an effective temperature from the fluctuation-dissipation theorem, computed from dynamical mean-field theory. We use the effective temperature to quantify the magnitude of the SGD noise as a function of the problem parameters. In the over-parametrized regime, where the training error vanishes, we measure the noise magnitude of SGD by computing the average distance between two replicas of the system with the same initialization and two different realizations of SGD noise. We find that the two noise measures behave similarly as a function of the problem parameters. Moreover, we observe that noisier algorithms lead to wider decision boundaries of the corresponding constraint satisfaction problem.

机器翻译，仅供参考