33 citations · 109 across the 39 of their papers we have counts for
51 papers
One-Shot Information Hiding and Compound Wiretap Channels
Yanxiao Liu, Cheuk Ting Li
In recent years, due to the growing reliance on large amounts of data that are communicated, analyzed, and utilized, which inherently contain sensitive and personal information, se…
Automated Proving of Shannon-Type Entropy Inequalities via Fine-Tuned Language Models and Guided Tree Search
Shing Yin Wong, Shaocheng Liu, Linqi Song +2
Proving Shannon-type entropy inequalities is a fundamental task in information theory that often requires constructing non-trivial linear combinations of known constraints, which i…
A Non-Probabilistic Game-Theoretic Information Theory Which Subsumes Probabilistic Channel Coding
Cheuk Ting Li
Probabilistic settings (e.g., vanishing-error channel coding) and non-probabilistic settings (e.g., zero-error channel coding and adversarial channels) were considered two related…
One-Cold Poisson Channel: A Simple Continuous-Time Channel with Zero Dispersion
Cheuk Ting Li
We introduce the one-cold Poisson channel (OCPC), where the transmitter chooses one of several frequency bands to attenuate at a time. In particular, the perfect OCPC, where the nu…
Coding-Logic Correspondence: Turning Information and Communication Networks into Logical Formulae via Hypergraph Heyting Algebra
Cheuk Ting Li
We propose using confusion hypergraph (hyperconfusion) as a model of information. In contrast to the conventional approach using random variables, we can now perform conjunction, d…
New Second-Order Achievability Bounds for Coding with Side Information via Type Deviation Convergence
Xiang Li, Cheuk Ting Li
We propose a framework for second-order achievability, called type deviation convergence, that is generally applicable to settings in network information theory, and is especially…
Joint Scheduling and Multiflow Maximization in Wireless Networks
Yanxiao Liu, Shenghao Yang, Cheuk Ting Li
Towards the development of 6G mobile networks, it is promising to integrate a large number of devices from multi-dimensional platforms, and it is crucial to have a solid understand…
Rejection-Sampled Linear Codes for Lossy Compression and Channel Simulation
Jianguo Zhao, Cheuk Ting Li
We show that linear codes combined with rejection sampling can yield a capacity-achieving scheme for simulating additive exchangeable noise channels. Specifically, our scheme achie…
Discrete Layered Entropy, Conditional Compression and a Tighter Strong Functional Representation Lemma
Cheuk Ting Li
We study a quantity called discrete layered entropy, which approximates the Shannon entropy within a logarithmic gap. Compared to the Shannon entropy, the discrete layered entropy…
Nonasymptotic Oblivious Relaying and Variable-Length Noisy Lossy Source Coding
Yanxiao Liu, Sepehr Heidari Advary, Cheuk Ting Li
The information bottleneck channel (or the oblivious relay channel) concerns a channel coding setting where the decoder does not directly observe the channel output. Rather, the ch…
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu +4
Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work,…
Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms
Parham Rezaei, Farzan Farnia, Cheuk Ting Li
The availability of multiple training algorithms and architectures for generative models requires a selection mechanism to form a single model over a group of well-trained generati…
Variable-Length Secret Key Agreement via Random Stopping Time
Junda Zhou, Cheuk Ting Li
We consider a key agreement setting where two parties observe correlated random sources, and want to agree on a secret key via public discussions. In order to allow the key length…
A Pair of Bayesian Network Structures has Undecidable Conditional Independencies
Cheuk Ting Li
Given a Bayesian network structure (directed acyclic graph), the celebrated d-separation algorithm efficiently determines whether the network structure implies a given conditional…
Universal Exact Compression of Differentially Private Mechanisms
Yanxiao Liu, Wei-Ning Chen, Ayfer Özgür +1
To reduce the communication cost of differential privacy mechanisms, we introduce a novel construction, called Poisson private representation (PPR), designed to compress and simula…
Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li +1
A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been…
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
Jingwei Zhang, Cheuk Ting Li, Farzan Farnia
The massive developments of generative model frameworks require principled methods for the evaluation of a model's novelty compared to a reference dataset. While the literature has…
One-Shot Coding over General Noisy Networks
Yanxiao Liu, Cheuk Ting Li
We present a unified one-shot coding framework designed for the communication and compression of messages among multiple nodes across a general acyclic noisy network. Our setting c…
Rejection-Sampled Universal Quantization for Smaller Quantization Errors
Chih Wei Ling, Cheuk Ting Li
We construct a randomized vector quantizer which has a smaller maximum error compared to all known lattice quantizers with the same entropy for dimensions 5, 6, ..., 48, and also h…
Pointwise Redundancy in One-Shot Lossy Compression via Poisson Functional Representation
Cheuk Ting Li
We study different notions of pointwise redundancy in variable-length lossy source coding. We present a construction of one-shot variable-length lossy source coding schemes using t…
Compression with Exact Error Distribution for Federated Learning
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li +1
Compression schemes have been extensively used in Federated Learning (FL) to reduce the communication cost of distributed learning. While most approaches rely on a bounded variance…
Communication-Efficient Laplace Mechanism for Differential Privacy via Random Quantization
Ali Moradi Shahmiri, Chih Wei Ling, Cheuk Ting Li
We propose the first method that realizes the Laplace mechanism exactly (i.e., a Laplace noise is added to the data) that requires only a finite amount of communication (whereas th…
A Characterization of Entropy as a Universal Monoidal Natural Transformation
Cheuk Ting Li
We show that the essential properties of entropy (monotonicity, additivity and subadditivity) are consequences of entropy being a monoidal natural transformation from the under cat…
A Poisson Decomposition for Information and the Information-Event Diagram
Cheuk Ting Li
Information diagram and the I-measure are useful mnemonics where random variables are treated as sets, and entropy and mutual information are treated as a signed measure. Although…