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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
An Information Theoretic Converse for the "Consecutive Co...
Tang Liu, Daniela Tuninetti · 2018-06-14 · via cs.IT updates on arXiv.org

Pliable Index CODing (PICOD) is a variant of the Index Coding (IC) problem in which a user is satisfied whenever it can successfully decode any one message that is not in its side information set, as opposed to a fixed pre-determined message. The complete--$S$ PICOD with $m$ messages, for $S\subseteq[0:m-1]$, has $n = \sum_{s\in S} \binom{m}{s}$ users with distinct side information sets. Past work on PICOD provided tight converse results when either the sender is constrained to use linear codes, or for some special classes of complete--$S$ PICOD. This paper provides a tight information theoretic converse result (i.e., no restriction to linear codes) for the so-called "consecutive complete--$S$" PICOD, where the set $S$ satisfies $S=[s_{min} : s_{max}]$ for some $0\leq s_{min} \leq s_{max} \leq m-1$. This result extends existing converse results and shows that linear codes have the smallest possible code length given by $\min(m-s_{\min},1+s_{\max})$. The central contribution is a novel proof technique rooted in combinatorics. The main idea is to consider all the messages a user can eventually successfully decode, in addition to its own desired message. This allows us to circumvent the necessity of essentially considering all possible assignments of desired messages for the users. The keystone of the proof is to show that, for the case of $S=\{s\}$ and $m = 2s+1$, there exists at least one user who can decode $s+1$ messages. From this, the extension to the "consecutive complete--$S$" PICOD follows.