Motivated by programmatic advertising optimization, we consider the task of
sequentially allocating budget across a set of resources. At every time step, a
feasible allocation is chosen and only a corresponding random return is
observed. The goal is to maximize the cumulative expected sum of returns. This
is a realistic model for budget allocation across subdivisions of marketing
campaigns, when the objective is to maximize the number of conversions. We
study direct search (aka pattern search) methods for linearly constrained and
derivative-free optimization in the presence of noise. Those algorithms are
easy to implement and particularly suited to constrained optimization. They
have not yet been analyzed from the perspective of cumulative regret. We
provide a regret upper-bound of the order of T 2/3 in the general case. Our
mathematical analysis also establishes, as a by-product, time-independent
regret bounds in the deterministic, unconstrained case. We also propose an
improved version of the method relying on sequential tests to accelerate the
identification of descent directions.

研究在预算分配中使用直接搜索方法，提出了一种改进的算法来加速梯度下降方向的识别，并将算法的累计遗憾度分析为 T 的 2/3 次方的上限。

盲资源分配的随机直接搜索方法的遗憾分析

Regret Analysis of the Stochastic Direct Search Method for Blind Resource Allocation

Uplift modeling is an emerging machine learning approach for estimating the
treatment effect at an individual or subgroup level. It can be used for
optimizing the performance of interventions such as marketing campaigns and
product designs. Uplift modeling can be used to estimate which users are likely
to benefit from a treatment and then prioritize delivering or promoting the
preferred experience to those users. An important but so far neglected use case
for uplift modeling is an experiment with multiple treatment groups that have
different costs, such as for example when different communication channels and
promotion types are tested simultaneously. In this paper, we extend standard
uplift models to support multiple treatment groups with different costs. We
evaluate the performance of the proposed models using both synthetic and real
data. We also describe a production implementation of the approach.

本文介绍了使用 uplift modeling 在多种处理组中分别分析处理效果的机器学习方法，通过人工合成数据以及真实数据进行模型测试，并提供了生产实施方案。