基于策略性智能体的处理分配

最优序贯治疗分配

Optimal sequential treatment allocation

在治疗分配问题中，被治疗的个体往往是顺序到达的。我们研究的问题，其中的政策制定者不仅有兴趣在预期的累积福利，但也关注的不确定性/风险的治疗结果。在开始时，甚至可能不知道要进行的治疗分配的总数。提出了一种使最优后悔策略达到最小最大的序贯处理策略。我们还表明，预期的次优治疗的数量只增长缓慢的治疗的数量。最后，我们研究了一个结果只观察延迟的设置。

序列网络博弈中的个性化治疗分配

Individualized Treatment Allocation in Sequential Network Games

本文提出通过评估平稳分布来优化治疗分配规则，以最大化社会福利，采用变分近似和贪心算法在序列决策博弈中实现均衡福利最大化，并通过模拟和印度微金融数据验证了显著的福利提升。

动态最优处理分配

Dynamically Optimal Treatment Allocation

本文研究在预算和容量约束下如何利用随机对照试验证据进行动态个性化决策，证明受限策略类的遗憾以 n^(-0.5) 速率衰减，并在职业培训应用中验证其优于静态方法。

Neyman allocation is minimax optimal for best arm identification with two arms

This note describes the optimal policy rule, according to the local asymptotic minimax regret criterion, for best arm identification when there are only two treatments. It is shown that the optimal sampling rule is the Neyman allocation, which allocates a constant fraction of units to each treatment in a manner that is proportional to the standard deviation of the treatment outcomes. When the variances are equal, the optimal ratio is one-half. This policy is independent of the data, so there is no adaptation to previous outcomes. At the end of the experiment, the policy maker adopts the treatm…

最小最大与贝叶斯最优自适应实验设计用于治疗选择

Minimax and Bayes Optimal Adaptive Experimental Design for Treatment Choice

本文提出了一种最小最大与贝叶斯最优的自适应实验设计方法，通过分阶段估计标准差并按比例分配治疗以优化治疗选择的后悔上界。

带协变量的函数型序贯处理分配

Functional Sequential Treatment Allocation with Covariates

本文研究带协变量的多臂老虎机问题，提出面向一般条件结果分布函数的处理分配框架，给出期望遗憾下界并构造近似极小极大最优策略。

Functional Sequential Treatment Allocation

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a qu…

预算约束下的PAC-贝叶斯处理分配

PAC-Bayesian Treatment Allocation Under Budget Constraints

本文针对预算约束下的处理分配问题，提出基于PAC-贝叶斯框架的灵活分配规则，推导了随机规则的非渐近泛化界和严格oracle型不等式，并验证了非随机模型聚合规则的优良属性。

竞争主体下的策略学习

Policy Learning with Competing Agents

本文研究容量约束下智能体策略性竞争的治疗分配问题，提出基于均值场均衡阈值的策略梯度一致估计器，并在半合成实验中验证其有效性。

ARMA-Design：部分可观测时间序列实验中 A/B 测试的最优处理分配策略

ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments

针对部分可观测在线时间序列实验，本文提出受控 ARMA 模型、小信号渐近框架及约束优化与强化学习两种最优 A/B 分配算法，并通过派单模拟器和真实网约车数据验证其性能。