116 citations · 140 across the 10 of their papers we have counts for
7 papers · 1 filter
Output-Dependent Gaussian Process State-Space Model
Zhidi Lin, Lei Cheng, Feng Yin +2
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition…
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Kai Chen, Qinglei Kong, Yijue Dai +4
Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning, next-generation data-driven communication sys…
Graph Neural Network for Large-Scale Network Localization
Wenzhong Yan, Di Jin, Zhidi Lin +1
Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems…
Optimally Combining Classifiers for Semi-Supervised Learning
Zhiguo Wang, Liusha Yang, Feng Yin +3
This paper considers semi-supervised learning for tabular data. It is widely known that Xgboost based on tree model works well on the heterogeneous features while transductive supp…
Scalable Learning Paradigms for Data-Driven Wireless Communication
Yue Xu, Feng Yin, Wenjun Xu +3
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and mode…
Multitask Gaussian Process with Hierarchical Latent Interactions
Kai Chen, Twan van Laarhoven, Elena Marchiori +2
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent lat…
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Kai Chen, Yijue Dai, Feng Yin +2
Spectral mixture (SM) kernels comprise a powerful class of generalized kernels for Gaussian processes (GPs) to describe complex patterns. This paper introduces model compression an…