# Technical Program

## Paper Detail

Paper Title Error Exponents in Distributed Hypothesis Testing of Correlations FR2.R3.4 Uri Hadar, Tel Aviv University, Israel; Jingbo Liu, Yury Polyanskiy, Massachusetts Institute of Technology, United States; Ofer Shayevitz, Tel Aviv University, Israel Error Exponents I Monge, Level 3 Friday, 12 July, 11:40 - 13:00 Friday, 12 July, 12:40 - 13:00 Click here to download the manuscript We study a distributed hypothesis testing problem where two parties observe i.i.d. samples from two $\rho$-correlated standard normal random variables $X$ and $Y$. The party that observes the $X$-samples can communicate $R$ bits per sample to the second party, that observes the $Y$-samples, in order to test between two correlation values. We investigate the best possible type-II error subject to a fixed type-I error, and derive an upper (impossibility) bound on the associated type-II error exponent. Our techniques include representing the conditional $Y$-samples as a trajectory of the Ornstein-Uhlenbeck process, and bounding the associated KL divergence using the subadditivity of the Wasserstein distance and the Gaussian Talagrand inequality.