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09/15/2023
Gaussian Processes with Linear Multiple Kernel: Spectrum Design and Distributed Learning for Multi-Dimensional Data
Gaussian processes (GPs) have emerged as a prominent technique for machi...
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01/21/2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model
Gaussian process state-space model (GPSSM) has attracted much attention ...
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12/15/2022
Output-Dependent Gaussian Process State-Space Model
Gaussian process state-space model (GPSSM) is a fully probabilistic stat...
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05/28/2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
Sparse modeling for signal processing and machine learning has been at t...
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11/27/2021
Preconditioned TBiCOR and TCORS Algorithms for Solving the Sylvester Tensor Equation
In this paper, the preconditioned TBiCOR and TCORS methods are presented...
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03/18/2021
Data-Driven Wireless Communication Using Gaussian Processes
Data-driven paradigms are well-known and salient demands of future wirel...
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10/22/2020
Graph Neural Network for Large-Scale Network Localization
Graph neural networks (GNNs) are popular to use for classifying structur...
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06/07/2020
Optimally Combining Classifiers for Semi-Supervised Learning
This paper considers semi-supervised learning for tabular data. It is wi...
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03/08/2020
FedLoc: Federated Learning Framework for Cooperative Localization and Location Data Processing
In this paper, we propose a new localization framework in which mobile u...
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03/01/2020
Scalable Learning Paradigms for Data-Driven Wireless Communication
The marriage of wireless big data and machine learning techniques revolu...
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07/05/2019
Gaussian Processes for Analyzing Positioned Trajectories in Sports
Kernel-based machine learning approaches are gaining increasing interest...
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06/06/2019
A General O(n^2) Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM
Hyper-parameter optimization remains as the core issue of Gaussian proce...
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04/21/2019
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Gaussian processes (GP) for machine learning have been studied systemati...
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03/09/2019
How Effectively Can Indoor Wireless Positioning Relieve Visual Tracking Pains: A Camera-Rao Bound Viewpoint
Visual tracking is fragile in some difficult scenarios, for instance, ap...
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02/13/2019