CAREER: Toward a Unified Approach to Universality in Information Processing

信号处理、通信和压缩应用中的一个主要挑战是构造接近理论性能极限的方案。源和信道特性的先验知识在实践中很少，这一事实需要这些计划是通用的。虽然针对不同问题的现行普遍办法显然具有许多共同的成分，但迄今为止，这些办法大多是分开审议的。该项目开发了一个统一的框架，包括理论，算法和实现方面的普适性问题。我们解决的问题，从去噪，Wyner-Ziv编码，联合信源信道编码，扫描和预测，强化学习等问题领域的通用方案。我们开发了一个统一的框架来解决这些问题，这有助于为新问题构建通用方案，并导致对现有问题的新见解。在应用方面，我们正在开发具有吸引力的复杂性的新算法范例，并将其应用于实践。我们还在开发用于评估新算法性能的分析工具。

A major challenge in signal processing, communication, and compression applications is the construction of schemes that approach the theoretical performance limits. The fact that a priori knowledge of source and channel characteristics is rarely available in practice necessitates that these schemes be universal. While it is clear that existing universal schemes for different problems share many common ingredients, thus far they have been considered mostly separately. This project develops a unified framework encompassing theoretical, algorithmic, and implementation aspects of the universality problem.We address questions concerning universal schemes in problem areas ranging from denoising, Wyner-Ziv coding, joint source-channel coding, scanning and prediction, to reinforcement learning. We develop a unified framework for addressing such questions, which facilitates the construction of universal schemes for new problems, and leads to new insights on existing ones. On the more applied side, we are developing new algorithmic paradigms that have attractive complexity, and applying them in practice. We are also developing analytic tools for assessing the performance of the new algorithms.