199 citations · 208 across the 10 of their papers we have counts for
11 papers
Minimum Distortion Quantization with Specified Output Distribution
Aolin Xu
We derive the optimal quantizer of a real-valued random variable with distribution such that 1) the distribution of the quantization output that can take values f…
Optimal Feedback Communication with Information Maximization and Distortion Minimization
Aolin Xu
We study the problem of optimally sending a real-valued source through multiple uses of a channel with feedback. First, we state a set of conditions that are sufficient for an enco…
Bayesian Learning for Dynamic Inference
Aolin Xu, Peng Guan
The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential…
Dynamic Inference
Aolin Xu
Traditional statistical estimation, or statistical inference in general, is static, in the sense that the estimate of the quantity of interest does not change the future evolution…
Anytime Decoding by Monte-Carlo Tree Search
Aolin Xu
An anytime decoding algorithm for tree codes using Monte-Carlo tree search is proposed. The meaning of anytime decoding here is twofold: 1) the decoding algorithm is an anytime alg…
Continuity of Generalized Entropy and Statistical Learning
Aolin Xu
We study the continuity property of the generalized entropy as a function of the underlying probability distribution, defined with an action space and a loss function, and use this…
Minimum Excess Risk in Bayesian Learning
Aolin Xu, Maxim Raginsky
We analyze the best achievable performance of Bayesian learning under generative models by defining and upper-bounding the minimum excess risk (MER): the gap between the minimum ex…
Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu, Maxim Raginsky
We derive upper bounds on the generalization error of a learning algorithm in terms of the mutual information between its input and output. The bounds provide an information-theore…
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation
Aolin Xu, Maxim Raginsky
We derive lower bounds on the Bayes risk in decentralized estimation, where the estimator does not have direct access to the random samples generated conditionally on the random pa…
Information-theoretic lower bounds for distributed function computation
Aolin Xu, Maxim Raginsky
We derive information-theoretic converses (i.e., lower bounds) for the minimum time required by any algorithm for distributed function computation over a network of point-to-point…
Converses for distributed estimation via strong data processing inequalities
Aolin Xu, Maxim Raginsky
We consider the problem of distributed estimation, where local processors observe independent samples conditioned on a common random parameter of interest, map the observations to…