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作者：Imai, Kosuke; Nakamura, Kentaro
作者单位：Harvard University; Harvard University; Harvard University
摘要：In this article, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation helps disentangle ...
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作者：Kent, Alexander; Berrett, Thomas B.; Yu, Yi
作者单位：University of Warwick
摘要：Most literature on differential privacy considers the item-level case where each user has a single observation, but a growing field is that of user-level privacy where each of the n users holds T observations and wishes to maintain the privacy of their entire collection. We derive a general minimax lower bound, which shows that, for locally private user-level estimation problems, the risk cannot, in general, be made to vanish for a fixed n even for T arbitrarily large. We then derive matching,...
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作者：Qiao, Xinghao; Wang, Zihan; Yao, Qiwei; Zhang, Bo
作者单位：University of Hong Kong; Tsinghua University; University of London; London School Economics & Political Science; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：The factor modeling for high-dimensional time series is powerful in discovering latent common components for dimension reduction and information extraction. Most available estimation methods can be divided into two categories: the covariance-based under asymptotically-identifiable assumption and the autocovariance-based with white idiosyncratic noise. This article follows the autocovariance-based framework and develops a novel weight-calibrated method to improve the estimation performance. It ...
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作者：Xiong, Xin; Guo, Zijian; Cai, Tianxi
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Zhejiang University; Harvard University; Harvard Medical School
摘要：Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on a new one, reducing the need for extensive data and training in each new context. Many existing transfer learning methods rely on leveraging information from source populations closely resembling the target population. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary sample...
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作者：Chen, Ling; Huang, Chengzhu; Gu, Yuqi
作者单位：Columbia University
摘要：This work focuses on the mixed membership models for multivariate categorical data widely used for analyzing survey responses and population genetics data. These grade of membership (GoM) models offer rich modeling power but present significant estimation challenges for high-dimensional polytomous data. Popular existing approaches, such as Bayesian MCMC inference, are not scalable and lack theoretical guarantees in high-dimensional settings. To address this, we first observe that data from thi...
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作者：Haupt, Andreas; Koyejo, Sanmi
作者单位：Stanford University
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作者：Guo, Wensheng; Wang, Tianhao
作者单位：University of Pennsylvania; Pennsylvania Medicine; Rush University
摘要：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 article, we propose...
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作者：Xu, Yuliang; Johnson, Timothy D.; Heitzeg, Mary; Kang, Jian
作者单位：University of Chicago; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Mediation analysis aims to separate the indirect effect through mediators from the direct effect of the exposure on the outcome. It is challenging to perform mediation analysis with neuroimaging data which involves high dimensionality, complex spatial correlations, sparse activation patterns and relatively low signal-to-noise ratio. To address these issues, we develop a new spatially varying coefficient structural equation model for Bayesian Image Mediation Analysis (BIMA). We define spatially...
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作者：Caner, Mehmet; Fan, Qingliang
作者单位：North Carolina State University; Chinese University of Hong Kong
摘要：This article explores the statistical properties of forming constrained optimal portfolios within a high-dimensional set of assets. We examine portfolios with tracking error constraints, those with simultaneous tracking error and weight restrictions, and portfolios constrained solely by weight. Tracking error measures portfolio performance against a benchmark (typically an index), while weight constraints determine asset allocation based on regulatory requirements or fund prospectuses. Our app...
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作者：Zhong, Han; Deng, Xun; Fang, Ethan X.; Yang, Zhuoran; Wang, Zhaoran; Li, Runze
作者单位：Peking University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Duke University; Yale University; Northwestern University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：While deep reinforcement learning has achieved tremendous successes in various applications, most existing works focus on maximizing the expected value of total return and ignore its inherent stochasticity. Such stochasticity is also known as the aleatoric uncertainty and is closely related to the notion of risk. This work makes the first attempt to study risk-sensitive deep reinforcement learning under the average reward setting with the variance risk criteria. Particularly, we focus on a var...
