14 citations · 16 across the 11 of their papers we have counts for
12 papers
Robust Bayesian Optimization with Q-Exponential Surrogates
Richard Cornelius Suwandi, Zhidi Lin, Feng Yin +1
Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box objectives, but standard BO methods often use Gaussian process (GP) surrogates whose Gaussi…
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
Zhidi Lin, Ying Li, Feng Yin +2
Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existi…
Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction
Yiyong Sun, Jiajun He, Zhidi Lin +3
Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel predic…
Preventing Model Collapse in Gaussian Process Latent Variable Models
Ying Li, Zhidi Lin, Feng Yin +1
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models commonly used for dimensionality reduction. However, common challenges in mo…
Regularization-Based Efficient Continual Learning in Deep State-Space Models
Yuanhang Zhang, Zhidi Lin, Yiyong Sun +2
Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single…
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
Zhidi Lin, Yiyong Sun, Feng Yin +1
The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in…
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
Zhidi Lin, Juan Maroñas, Ying Li +2
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaus…
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
Richard Cornelius Suwandi, Zhidi Lin, Feng Yin +2
Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyper-parameter optimization. This…
Towards Flexibility and Interpretability of Gaussian Process State-Space Model
Zhid Lin, Feng Yin, Juan Maroñas
The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade. However, the standard GP with a preliminary kernel, such as the squared exp…
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…
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…
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
Feng Yin, Zhidi Lin, Yue Xu +5
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big da…