14 citations · 16 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
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…