Today's online advertisers procure digital ad impressions through interacting
with autobidding platforms: advertisers convey high level procurement goals via
setting levers such as budget, target return-on-investment, max cost per click,
etc.. Then ads platforms subsequently procure impressions on advertisers'
behalf, and report final procurement conversions (e.g. click) to advertisers.
In practice, advertisers may receive minimal information on platforms'
procurement details, and procurement outcomes are subject to non-stationary
factors like seasonal patterns, occasional system corruptions, and market
trends which make it difficult for advertisers to optimize lever decisions
effectively. Motivated by this, we present an online learning framework that
helps advertisers dynamically optimize ad platform lever decisions while
subject to general long-term constraints in a realistic bandit feedback
environment with non-stationary procurement outcomes. In particular, we
introduce a primal-dual algorithm for online decision making with
multi-dimension decision variables, bandit feedback and long-term uncertain
constraints. We show that our algorithm achieves low regret in many worlds when
procurement outcomes are generated through procedures that are stochastic,
adversarial, adversarially corrupted, periodic, and ergodic, respectively,
without having to know which procedure is the ground truth. Finally, we
emphasize that our proposed algorithm and theoretical results extend beyond the
applications of online advertising.

本文提出了一种在线学习框架，利用多维度的决策变量、赌博反馈和长期不确定性约束，帮助广告客户在非平稳采购结果的真实赌博反馈环境中动态优化广告平台的杠杆决策，取得低损失结果。