Author names are in alphabetical order.
2024
An entropic inequality in finite Abelian groups analogous to the unified Brascamp-Lieb and Entropy Power Inequality, K. Lau and C. Nair, Will be presented at 2024 IEEE International Symposium on Information Theory.
Summary: This paper develops a technique to prove the extremality of Haar distributions for a family of discrete entropic inequalities in finite Abelian groups, which is an analogy to a family of differential entropic inequalities that unifies the Entropy Power Inequality and the Brascamp-Lieb inequalities.
2023
A mutual information inequality and some applications, K. Lau, C. Nair, and D. Ng, Published at 2023 IEEE Transactions on Information Theory, vol. 69, no. 10, pp. 6210-6220. IEEE Xplore
Summary: This paper derives an inequality relating linear combinations of mutual information between subsets of mutually independent random variables and an auxiliary random variable, and it states some new results and generalizations and new proofs of known results.
Information inequalities via ideas from additive combinatorics, K. Lau and C. Nair, Presented at 2023 IEEE International Symposium on Information Theory, pp. 2452-2457. IEEE Xplore
Summary: This paper establishes formal equivalences between some families of inequalities in additive combinatorics and entropic ones, and provides an information-theoretic characterization of the magnification ratio in the graph theory.
2022
Uniqueness of local maximizers for some non-convex log-determinant optimization problems using information theory, K. Lau, C. Nair, and C. Yao, Presented at 2022 IEEE International Symposium on Information Theory, pp. 432-437. IEEE Xplore
Summary: This paper shows that a family of non-convex optimizations involving linear combinations of log-determinants of positive definite matrices has a unique local maximizer, which is related to the capacity region of the vector Gaussian broadcast channel.
A mutual information inequality and some applications, K. Lau, C. Nair, and D. Ng, Presented at 2022 IEEE International Symposium on Information Theory, pp. 951-956. IEEE Xplore
Summary: This paper derives an inequality relating linear combinations of mutual information between subsets of mutually independent random variables and an auxiliary random variable, and it states some new results and generalizations and new proofs of known results.
2021
Concavity of output relative entropy for channels with binary inputs, Q. Ding, K. Lau,C. Nair, and Y. Wang, 2021 IEEE International Symposium on Information Theory, pp.2738-2743.
Summary: This paper generalizes a convexity result due to Wyner and Ziv to channels with binary inputs and arbitrary outputs. This results in a convex reformulation of some non-convex optimization problems that arise naturally in multi-user information theory.