Mingjun Zhong

Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations

Approximations for Binary Gaussian Process Classification

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo

Stan: A Probabilistic Programming Language

A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing

Enhanced Cyber-Physical Security in Internet of Things Through Energy Auditing

Advances in Variational Inference

Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework

Review on deep learning applications in frequency analysis and control of modern power system

Industrial Internet-of-Things Security Enhanced With Deep Learning Approaches for Smart Cities

Battery Lifetime Prognostics

Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors

A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

Graph-based approach for airborne light detection and ranging segmentation

Generalizing from a Few Examples

Reversible Jump MCMC for Non-Negative Matrix Factorization

Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring

Transfer Learning for Non-Intrusive Load Monitoring

A variational method for learning sparse Bayesian regression

Classifying EEG for brain computer interfaces using Gaussian processes

Efficient Gradient-Free Variational Inference using Policy Search