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面向人类反馈的情境在线不确定性感知偏好学习AI 解读

Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human preferences. In this paper, we propose a novel statistical framework to simultaneously conduct the online decision-making and statistical inference on the optimal model using human preference data based on dynamic contextual information. Our approach introduces an efficient decision strategy that achieves both the optimal regret bound…

通过最佳子集选择实现小行动空间下近乎维数无关的稀疏线性赌博机AI 解读

We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendations, online advertisements, and personalized medicine. However, it is very challenging to balance the exploration and exploitation tradeoff. We modify the LinUCB…

面向公平性的最优个体化处理规则学习AI 解读

There has recently been a surge on the methodological development for optimal individualized treatment rule (ITR) estimation. The standard methods in the literature are designed to maximize the potential average performance (assuming larger outcomes are desirable). A notable drawback of the standard approach, due to heterogeneity in treatment response, is that the estimated optimal ITR may be suboptimal or even detrimental to certain disadvantaged subpopulations. Motivated…

高维纵向数据的显著性检验AI 解读

This paper concerns statistical inference for longitudinal data with ultrahigh dimensional covariates. We first study the problem of constructing confidence intervals and hypothesis tests for a low dimensional parameter of interest. The major challenge is how to construct a powerful test statistic in the presence of high-dimensional nuisance parameters and sophisticated within-subject correlation of longitudinal data. To deal with the challenge, we propose a new quadratic…

利用稀疏线性规划优化两阶段自适应富集随机试验设计AI 解读

Summary Adaptive enrichment designs involve preplanned rules for modifying enrolment criteria based on accruing data in a randomized trial. We focus on designs where the overall population is partitioned into two predefined subpopulations, e.g. based on a biomarker or risk score measured at baseline. The goal is to learn which populations benefit from an experimental treatment. Two critical components of adaptive enrichment designs are the decision rule for modifying…

高维比例风险模型的检验与置信区间AI 解读

Summary The paper considers the problem of hypothesis testing and confidence intervals in high dimensional proportional hazards models. Motivated by a geometric projection principle, we propose a unified likelihood ratio inferential framework, including score, Wald and partial likelihood ratio statistics for hypothesis testing. Without assuming model selection consistency, we derive the asymptotic distributions of these test statistics, establish their semiparametric…

海量基因组数据挖掘：半参数主题建模方法AI 解读

Characterizing the functional relevance of transcription factors (TFs) in different biological contexts is pivotal in systems biology. Given the massive amount of genomic data, computational identification of TFs is emerging as a useful approach to bridge functional genomics with disease risk loci. In this article, we use large-scale gene expression and chromatin immunoprecipitation (ChIP) data corpuses to conduct high-throughput TF-biological context association analysis.…