RI: SMALL: Robust Inference and Influence in Dynamic Environments

许多应用领域，如在线内容推荐、医学试验以及智能基础设施中的物流和运营，都要求从业者在几乎没有环境信息和所选操作的最优性的情况下顺序地做出决策。在有限的测量预算下，自适应数据收集可以在测量现象、机器学习的最新进展为如何使用过去的观测结果来指导未来测量的规划提供了丰富的见解。大多数最先进的方法假设观测来自具有固定概率特性的环境。然而，在实践中，这样的环境往往是例外，而不是规则。为了解决这个问题，实践者用特殊的例外和经验法则机制来补充现有的算法，但是依赖于逻辑学来解释脆弱的假设本身可能是脆弱的，并对整个实验提出质疑。该项目的目标是开发具有性能保证的算法，用于在动态和不可预测的环境中自适应地收集数据，目标是进行更快、更便宜的鲁棒推断。该项目的技术议程将通过引入上下文非随机纯探索框架来推进最先进的技术，该框架扩展了上下文无关的非随机最佳臂识别的最新进展，并在可能时利用可用的上下文信息，同时在非随机环境中给出鲁棒的保证。 通过采用两全其美的方法，该项目还将在自适应推理算法的设计中引入弹性，并通过将模型错误规范构建到框架中来影响。这将允许简单的模型被用作近似模型，用于在模型准确时进行推断和影响，而不是在模型不准确时进行误导。该框架还将扩展到在上下文非静态或流数据环境的更具挑战性的设置中对机器学习模型进行超参数调整（例如，用于"边缘计算“的移动的设备上的联合学习），从而提供了在若干应用领域中具有实用性的理论进步。该项目将在制定严格的理论框架过程中，根据真实的世界数据进行实验。伴随着技术议程的是一个综合的研究和教育计划，其中包括跨学科的本科生和格拉德课程开发，利用实验平台，以及学生与行业接触的机会。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Many application domains, such as online content recommendation, medical trials, and logistics and operations in intelligent infrastructure require the practitioner to sequentially make decisions given little information about the environment and the optimality of selected actions.On a limited measurement budget, adaptive data collection can make the difference between measuring a phenomenon, or missing it. Recent advances in machine learning have provided rich insights into ways of using past observations to guide planning of future measurements. Most state-of-the-art approaches assume observations arise from an environment with fixed probabilistic characteristics. Yet, in practice, environments such as these tend to be the exception and not the rule. To handle this, practitioners supplement existing algorithms with ad-hoc exceptions and rule of thumb mechanisms, but relying on heuristics to account for brittle assumptions may itself be brittle and call into question the entire experiment. The objective of this project is to develop algorithms with performance guarantees for collecting data adaptively in dynamic and unpredictable environments with the goal of making robust inferences, faster and cheaper.The technical agenda of this project will advance the state-of-the-art by introducing a contextual non-stochastic pure exploration framework that extends recent advances in context-free non-stochastic best-arm identification, and exploits available contextual information when possible while simultaneously giving robust guarantees in non-stochastic environments. By taking a best-of-both-worlds approach, the project will also introduce resilience into the design of algorithms for adaptive inference and influence by building model misspecification into the framework. This will allow for simple models to be used as an approximate model for inference and influence when the model is accurate, but not mislead when it is not. The framework will also extend to hyperparameter tuning of machine learning models in the more challenging setting of contextual non-stationary or streaming-data environments (e.g., federated learning on mobile devices for ``edge computing'') thereby providing theoretical advancements with utility in several application domains. The project will conduct experiments informed by real world data in its development of a rigorous theoretical framework. Accompanying the technical agenda is an integrated research and education plan that includes cross-disciplinary undergrad and grad course development leveraging the experimental platform as well as student opportunities to engage with industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.