论文检索

面向大规模神经影像数据的可扩展贝叶斯图像对标量回归AI 解读

Bayesian Image-on-Scalar Regression (ISR) provides flexible, uncertainty-aware neuroimaging analysis. However, applying ISR to large-scale datasets such as the UK Biobank is challenging due to intensive computational demands and the need to handle subject-specific brain masks rather than a common mask. We propose a novel Bayesian ISR model that scales efficiently while accommodating these inconsistent masks. Our method leverages Gaussian process priors with salience area…

强化学习变量选择的序贯 Knockoffs 方法AI 解读

In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state larger than necessary, e.g., by concatenating measurements over contiguous time points. However, needlessly increasing the dimension of the state may slow learning and obfuscate the learned policy. We introduce the notion of a…

个体数据与汇总统计量的半参数有效融合AI 解读

Suppose we have individual data from an internal study and various summary statistics from relevant external studies. External summary statistics have the potential to improve statistical inference for the internal population; however, it may lead to efficiency loss or bias if not used properly. We study the fusion of individual data and summary statistics in a semiparametric framework to investigate the efficient use of external summary statistics. Under a weak…

生成式贝叶斯推断中的树型多臂老虎机方法AI 解读

In generative models with intractable likelihood, Approximate Bayesian Computation (ABC) is often the tool of last resort for inference. However, ABC demands many prior parameter proposals to keep only the small fraction of them that meet the acceptance criterion. To accelerate ABC rejection sampling, this paper develops a self-aware framework that learns from past proposals: both acceptances and rejections. We apply recursive partitioning classifiers on the ABC lookup table…

结合目标学习推断的在线元层级自适应设计框架AI 解读

Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In practice, experimenters often consider multiple candidate designs, each with distinct trade-offs. However, typically only one design is implemented at a time, leaving benefits and costs of alternative designs unobserved and unquantified. To address this, we propose a novel meta-level adaptive…

利用充分维度关联进行高维统计推断与变量选择AI 解读

Simultaneous variable selection and statistical inference is challenging in high-dimensional data analysis. Most existing post-selection inference methods require explicitly specified regression models, which are often linear, as well as sparsity in the regression model. The performance of such procedures can be poor under either misspecified nonlinear models or a violation of the sparsity assumption. In this article, we propose a sufficient dimension association (SDA)…

高维空间极值的空间尺度感知尾部依赖建模AI 解读

Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence class over the entire spatial domain. To accurately characterize dependence in extreme events, we propose a mixture model that achieves flexible dependence properties and allows high-dimensional inference (∼600 spatial locations in our data example) for extremes of spatial processes. We modify the…

少即是多：二元反馈可能更有利于排序恢复AI 解读

Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We propose a general parametric framework for modeling ordinal paired comparisons without ties. The model adopts a generalized additive structure, featuring a link function that quantifies the preference difference…

随机稠密分布偏移下的分布外泛化AI 解读

Many existing approaches for estimating parameters in settings with distributional shifts operate under an invariance assumption. For example, under covariate shift, it is assumed that p(y|x) remains invariant. We refer to such distribution shifts as sparse, since they may be substantial but affect only a part of the data generating system. In contrast, in various real-world settings, shifts might be dense. More specifically, these dense distributional shifts may arise…

深度学习中分支与嵌套超参数的贝叶斯优化AI 解读

Hyperparameter optimization plays a crucial role in the success of neural networks as hyperparameters directly control the behavior and performance of the training algorithms. To obtain efficient tuning, Bayesian optimization based on Gaussian process is widely used. Despite numerous applications in deep learning, the existing methods rely on a convenient but restrictive assumption that the tuning parameters are independent of each other. However, tuning parameters with…

微分方程建模中的非参数先验学习AI 解读

This article addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a prediction function of the prior distribution from historical training datasets. We introduce hyper-prior and hyper-posterior distributions and derive a generalization error estimate, which accommodates data-dependent priors by extending the…

多元混合数据的最大二项似然方法AI 解读

Multivariate mixture data analysis presents numerous challenges and constitutes a vital area of interest in the fields of statistics and data science. Research into multivariate mixture structures holds relevance across diverse application domains and plays a pivotal role in the advancement of artificial intelligence and machine learning. In this article, we focus on nonparametric estimation techniques for multivariate mixture data. Specifically, we assume a known number of…

自适应划分因子分析AI 解读

Factor Analysis has traditionally been utilized across diverse disciplines to extrapolate latent traits that influence the behavior of multivariate observed variables. Historically, the focus has been on analyzing data from a single study, neglecting the potential study-specific variations present in data from multiple studies. Multi-study factor analysis has emerged as a recent methodological advancement that addresses this gap by distinguishing between latent traits shared…

将预测分布转化为信息性先验AI 解读

“When complex Bayesian models exhibit implausible behaviour, one solution is to assemble available information into an informative prior. Challenges arise as prior information is often only available for the observable quantity, or some model-derived marginal quantity, rather than directly pertaining to the (usually latent) parameters in our model. We propose a method for translating available prior information, in the form of an elicited distribution for the observable or…

小区域估计中的双重稳健方法AI 解读

In the context of robust small area estimation (SAE), there are two types of robustness considerations, robustness against model misspecification and robustness against outliers. We propose a method of SAE that has both types of robustness features. The method combines the idea of observed best prediction (OBP), which is known to be more robust against model misspecification than the traditional best linear unbiased prediction (EBLUP) method, and the method of density power…

可解释的市场微观结构噪声AI 解读

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the…

通过变分自编码器快速灵活地模拟空间极值过程AI 解读

Many real-world processes have complex tail dependence structures that cannot be adequately characterized using classical Gaussian processes. Alternatively, models motivated by extreme-value theory exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions using classical methods. In this paper, we extend the boundaries on computation and modeling of high-dimensional spatial extremes by integrating a new…

广义单调性对平均处理效应的识别力AI 解读

In the context of a binary outcome, treatment, and instrument, Balke and Pearl establish that the monotonicity condition of Imbens and Angrist has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This article shows…

全凹回归AI 解读

Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most existing methods—except additive models—impose too weak restrictions, often leading to overfitting in high dimensions. Conversely, additive models can be too rigid, failing to capture covariate interactions. This paper introduces a novel multivariate shape-constrained regression approach based on…

通过随机梯度下降对情境老虎机进行在线统计推断AI 解读

With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for an online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes…

控制错误发现率的局部关联搜索AI 解读

We introduce local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. By expanding upon the model-X knockoff filter, we show how to adaptively discover these local associations, all while controlling the false discovery rate. Our enhanced inferences can help explain sample heterogeneity and uncover interactions, making better use of the capabilities offered by modern…

含纵向协变量的生存分析半参数联合建模AI 解读

Characterizing the association between survival time and the dynamic patterns of a longitudinal covariate trajectory is of particular interest in many studies. Classical time-dependent survival models focus mainly on the link between the concurrent covariate value and the instantaneous hazard function. Consequently, the conditional survival function is often not properly defined on the whole time range, which causes difficulty in model estimation and interpretation. In this…

系统性与共同风险驱动的边际期望副作用AI 解读

System-wide stress events (SWSE) can occur in social, economic, financial, environmental, medical, and many other systems. An SWSE can cause severe damage to some or all members in the system. Understanding and measuring the marginal expected side-effect (MES) for each member caused by an SWSE can lead to better risk management and decision-making. This paper defines an SWSE as a scenario in which either a representative systematic index or the index of the worst-performing…

情境动态定价：算法、最优性与局部差分隐私约束AI 解读

We study contextual dynamic pricing problems where a firm sells products to T sequentially-arriving consumers, behaving according to an unknown demand model. The firm aims to minimize its regret over a clairvoyant that knows the model in advance. The demand follows a generalized linear model (GLM), allowing for stochastic feature vectors in Rd encoding product and consumer information. We first show the optimal regret is of order dT, up to logarithmic factors, improving…