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具有用户与商品双重情境信息的低秩在线动态选品AI 解读

As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this article, we consider the dynamic assortment problem with dual contexts—user and item features. In high-dimensional scenarios, the quadratic growth of dimensions…

矩阵情境在线决策中的统计推断AI 解读

The study of online decision-making problems that leverage contextual information has drawn notable attention due to their significant applications in fields ranging from healthcare to autonomous systems. In modern applications, contextual information can be rich and is often represented as a matrix. Moreover, while existing online decision algorithms mainly focus on reward maximization, less attention has been devoted to statistical inference. To address these gaps, in this…

基于隐私化成对排序的速率最优排序聚合AI 解读

In various real-world scenarios, such as recommender systems and political surveys, pairwise rankings are commonly collected and used for rank aggregation to derive an overall ranking of items. However, preference rankings can reveal individuals’ personal preferences, highlighting the need to protect them from exposure in downstream analysis. In this article, we address the challenge of preserving privacy while ensuring the utility of rank aggregation based on pairwise…

多维在线决策的随机低秩张量老虎机AI 解读

Multi-dimensional online decision making plays a crucial role in many real applications such as online recommendation and digital marketing. In these problems, a decision at each time is a combination of choices from different types of entities. To solve it, we introduce stochastic low-rank tensor bandits, a class of bandits whose mean rewards can be represented as a low-rank tensor. We consider two settings, tensor bandits without context and tensor bandits with context. In…

面向始终有效高维动态定价的在线正则化AI 解读

–Devising a dynamic pricing policy with always valid online statistical learning procedures is an important and as yet unresolved problem. Most existing dynamic pricing policies, which focus on the faithfulness of adopted customer choice models, exhibit a limited capability for adapting to the online uncertainty of learned statistical models during the pricing process. In this article, we propose a novel approach for designing a dynamic pricing policy based on regularized…

强化学习策略评估的在线自助法推断AI 解读

The recent emergence of reinforcement learning (RL) has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing methods for inference in online learning are restricted to settings involving independently sampled observations, while inference methods in RL have so far been limited to the batch setting. The bootstrap is a flexible and efficient approach for statistical inference in online learning…

协变量辅助的稀疏张量补全AI 解读

We aim to provably complete a sparse and highly missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users’ click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on nonmissing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often…

部分观测动态张量响应回归AI 解读

In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness,…

混合效应时变网络模型及其在脑连接分析中的应用AI 解读

Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep…

动态张量聚类AI 解读

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. There is also a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we propose a new dynamic tensor clustering method that works for a general-order dynamic tensor, and enjoys both strong statistical…

具有理论保证的稀疏张量分解AI 解读

Summary We propose a novel sparse tensor decomposition method, namely the tensor truncated power method, that incorporates variable selection in the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family of high dimensional latent variable models, including high dimensional Gaussian mixtures and mixtures of sparse regressions. A thorough theoretical…

稳定化最近邻分类器及其统计性质AI 解读

The stability of statistical analysis is an important indicator for reproducibility, which is one main principle of the scientific method. It entails that similar statistical conclusions can be reached based on independent samples from the same underlying population. In this article, we introduce a general measure of classification instability (CIS) to quantify the sampling variability of the prediction made by a classification method. Interestingly, the asymptotic CIS of…