1 citations · 1 across the 5 of their papers we have counts for
6 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…