Many aspects of human learning have been proposed as a process of
constructing mental programs: from acquiring symbolic number representations to
intuitive theories about the world. In parallel, there is a long-tradition of
using information processing to model human cognition through Rate Distortion
Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental
representations take the form of programs. In this work, we adapt RDT by
proposing a three way trade-off among rate (description length), distortion
(error), and computational costs (search budget). We use simulations on a
melody task to study the implications of this trade-off, and show that
constructing a shared program library across tasks provides global benefits.
However, this comes at the cost of sensitivity to curricula, which is also
characteristic of human learners. Finally, we use methods from partial
information decomposition to generate training curricula that induce more
effective libraries and better generalization.

我们通过在多个任务之间构建共享的程序库，提出了一种折中方法，其中在描述长度、误差和计算成本之间进行三向权衡，从而适应了心理表示采用编程形式的情况，该方法在仿真的旋律任务中表明共享程序库提供了全局权益，但这是以对教材敏感的代价为代价，这也是人类学习者的特点。最后，我们使用部分信息分解的方法生成能够引起更有效的程序库和更好泛化的训练教材。

与速率失真理论相协调的程序归纳

Harmonizing Program Induction with Rate-Distortion Theory

Blau and Michaeli recently introduced a novel concept for inverse problems of
signal processing, that is, the perception-distortion tradeoff. We introduce
their tradeoff into the rate distortion theory of lossy source coding in
information theory, and clarify the tradeoff among information rate, distortion
and perception for general information sources.

本文介绍了 Blau 和 Michaeli 最近引入的一个新概念，在信息理论中，将他们的 tradeoff 引入到有损源编码的速率失真理论中，并阐明了对于普遍信息源的信息速率、失真和感知之间的权衡。

将感知扭曲的权衡引入一般信息源的速率 - 失真理论

Introducing the Perception-Distortion Tradeoff into the Rate-Distortion  Theory of General Information Sources

Learning cooperative policies for multi-agent systems is often challenged by
partial observability and a lack of coordination. In some settings, the
structure of a problem allows a distributed solution with limited
communication. Here, we consider a scenario where no communication is
available, and instead we learn local policies for all agents that collectively
mimic the solution to a centralized multi-agent static optimization problem.
Our main contribution is an information theoretic framework based on rate
distortion theory which facilitates analysis of how well the resulting fully
decentralized policies are able to reconstruct the optimal solution. Moreover,
this framework provides a natural extension that addresses which nodes an agent
should communicate with to improve the performance of its individual policy.

本文提出了基于信息论的速率畸变理论框架，它使得我们能够分析完全分散的政策能够重构最优解的程度，同时，该框架提供了自然的扩展解决了一个代理应该与哪些节点通信以提高其个体策略的性能的问题。