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.
Convex Optimization for Linear Query Processing under Approximate Differential Privacy
South China University of Technology（华南理工大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Illinois at Singapore Pte. Ltd（伊利诺伊大学新加坡私人有限公司）；Foshan University（佛山科学技术学院）
Make Up Your Mind: The Price of Online Queries in Differential Privacy
Harvard University John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Yale University（耶鲁大学）；Northeastern University College of Computer and Information Science（东北大学计算机与信息科学学院）
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures
Department of Computer Science, Indian Institute of Technology Bombay（印度理工学院孟买分校计算机科学系）；Department of Computer Science, University of Texas at Dallas（得克萨斯大学达拉斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of Washington, Seattle（华盛顿大学西雅图分校电气与计算机工程系）；School of Technology and Computer Science, Tata Institute of Fundamental Research, Mumbai（塔塔基础研究所孟买技术与计算机科学学院）
