Bayesian optimization is a popular tool for optimizing time-consuming objective functions with limited number of function evaluations. In real-life applications like engineering design, the designer often wants to take multiple objectives as well input uncertainty into account find set robust solutions. While this an active topic in single-objective optimization, it less investigated multi-obje...

Recently, multi-hop reasoning over incomplete Knowledge Graphs (KGs) has attracted wide attention due to its desirable interpretability for downstream tasks, such as question answer and knowledge graph completion. Multi-Hop is a typical sequential decision problem, which can be formulated Markov process (MDP). Subsequently, some reinforcement learning (RL) based approaches are proposed proven e...

This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through lens reliability and robustness. Deep techniques adopt frequentist framework, are known provide poorly calibrated decisions that do not reproduce true uncertainty caused by limitations in size training data. Bayesian learning, while principle capable addressing ...

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare compatibility models given observed data. However, describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten parameters are incorporated in model. With such complex model, likelihood evaluation for each Monte Carlo sampling point become...

A multiscale, non-parametric, Bayesian framework for identification of model parameters Motivation Bayesian Paradigm Nonparametric prior Inference Numerical Results Prediction Motivation T (0) = T 0 q(1) = q 0 l = 1 c(x) =?    d dx −c(x) dT dx = 0 T (0) = T 0 q(1) = −c(x) dT dx x=1 = q 0 (1) 2 / 56 A multiscale, non-parametric, Bayesian framework for identification of model parameters Motiva...

Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large network is to be used for the first time, or when the network is complex or has just been updated. Tools to assist users in the analysis of Bayesian networks can help. In this paper, we introduce a novel general framework and tool for answering exploratory queries over Bayesian networks. The frame...