标题： 在变化环境中的决策制定：鲁棒性、基于查询的学习和 differential privacy 显示英文标题

标题： Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy

摘要： 我们研究了在随时间变化的环境中进行交互式决策的问题，该环境的变化受到给定约束条件的限制。 我们提出了一种框架，称为\textit{基于结构化观测的混合决策-making}（混合DMSO），它在决策的随机性和对抗性设定之间提供了插值。 在这个框架内，我们可以分析局部差分隐私（LDP）决策制定、基于查询的学习（特别是SQ学习）、以及在相同伞状结构下的鲁棒和平滑决策制定，基于决策-估计系数（DEC）的变体推导出上下界。 我们进一步建立了DEC的行为、SQ维数、局部最小极大复杂度、可学习性和联合差分隐私之间的强联系。 为了展示该框架的能力，我们在LDP约束下为上下文-bandit问题提供了新的结果。 显示英文摘要

摘要： We study the problem of interactive decision making in which the underlying environment changes over time subject to given constraints. We propose a framework, which we call \textit{hybrid Decision Making with Structured Observations} (hybrid DMSO), that provides an interpolation between the stochastic and adversarial settings of decision making. Within this framework, we can analyze local differentially private (LDP) decision making, query-based learning (in particular, SQ learning), and robust and smooth decision making under the same umbrella, deriving upper and lower bounds based on variants of the Decision-Estimation Coefficient (DEC). We further establish strong connections between the DEC's behavior, the SQ dimension, local minimax complexity, learnability, and joint differential privacy. To showcase the framework's power, we provide new results for contextual bandits under the LDP constraint.