Minimum Entropy Coupling with Bottleneck

TL;DR

This paper introduces MEC-B, a new lossy compression framework that combines minimum entropy coupling with a bottleneck to control stochasticity, enabling efficient joint compression and retrieval under distributional shifts.

Contribution

It extends classical minimum entropy coupling by integrating a bottleneck, providing a novel framework and algorithms for controlled stochasticity in compression tasks.

Findings

The greedy algorithm for EBIM guarantees performance.

Theoretical analysis characterizes solutions near functional mappings.

Experiments demonstrate improved trade-offs in Markov Coding Games.

Abstract

This paper investigates a novel lossy compression framework operating under logarithmic loss, designed to handle situations where the reconstruction distribution diverges from the source distribution. This framework is especially relevant for applications that require joint compression and retrieval, and in scenarios involving distributional shifts due to processing. We show that the proposed formulation extends the classical minimum entropy coupling framework by integrating a bottleneck, allowing for a controlled degree of stochasticity in the coupling. We explore the decomposition of the Minimum Entropy Coupling with Bottleneck (MEC-B) into two distinct optimization problems: Entropy-Bounded Information Maximization (EBIM) for the encoder, and Minimum Entropy Coupling (MEC) for the decoder. Through extensive analysis, we provide a greedy algorithm for EBIM with guaranteed performance, and characterize the optimal solution near functional mappings, yielding significant theoretical insights into the structural complexity of this problem. Furthermore, we illustrate the practical application of MEC-B through experiments in Markov Coding Games (MCGs) under rate limits. These games simulate a communication scenario within a Markov Decision Process, where an agent must transmit a compressed message from a sender to a receiver through its actions. Our experiments highlight the trade-offs between MDP rewards and receiver accuracy across various compression rates, showcasing the efficacy of our method compared to conventional compression baseline.