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Xiaobing Dai

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

Streaming Generated Gaussian Process Experts for Online Learning and Control

Zewen Yang
Dongfa Zhang
Xiaobing Dai
Fengyi Yu
Chi Zhang
Bingkun Huang
Hamid Sadeghian
Sami Haddadin

Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additionally, GPs support online learning by efficiently incorporating new data with polynomial-time computation, making them well-suited for safety-critical dynamical systems that require rapid adaptation. However, the inference and online updates of exact GPs, when processing streaming data, incur cubic computation time and quadratic storage memory complexity, limiting their scalability to large datasets in real-time settings. In this paper, we propose a streaming kernel-induced progressively generated expert framework of Gaussian processes (SkyGP) that addresses both computational and memory constraints by maintaining a bounded set of experts, while inheriting the learning performance guarantees from exact Gaussian processes. Furthermore, two SkyGP variants are introduced, each tailored to a specific objective, either maximizing prediction accuracy (SkyGP-Dense) or improving computational efficiency (SkyGP-Fast). The effectiveness of SkyGP is validated through extensive benchmarks and real-time control experiments demonstrating its superior performance compared to state-of-the-art approaches.

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AAAI Conference 2025 Conference Paper

Asynchronous Distributed Gaussian Process Regression

Zewen Yang
Xiaobing Dai
Sandra Hirche

In this paper, we address a practical distributed Bayesian learning problem with asynchronous measurements and predictions due to diverse computational conditions. To this end, asynchronous distributed Gaussian process (AsyncDGP) regression is proposed, which is the first effective online distributed Gaussian processes (GPs) approach to improve the prediction accuracy in real-time learning tasks. By leveraging the devised evaluation criterion and established prediction error bounds, AsyncDGP enables the distinction of contributions of each model for prediction ensembling using aggregation strategy. Furthermore, we extend its utility to dynamic systems by introducing a learning-based control law, ensuring guaranteed control performance in safety-critical applications. Additionally, a networked online learning simulation platform for distributed GPs, namely online GP gym (GPgym), is introduced for testing the performance of learning and control of dynamical systems. Numerical simulations within GPgym across regression tasks with real-world data sets and dynamical control scenarios demonstrate the effectiveness and applicability of AsyncDGP.

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EAAI Journal 2025 Journal Article

Safe event-triggered control of unmanned surface vehicles with Gaussian processes: Resilience in denial of service attacks and uncertain dynamics

Zewen Yang
Xiaobing Dai
Liang Fang
Jiajia Zhou
Zheping Yan

This paper addresses critical security challenges in cyber–physical systems arising from uncertain system dynamics and cyberattacks by proposing a learning-based event-triggered control protocol for networked unmanned surface vehicles (USVs). Leveraging Gaussian process regression, the proposed data-driven approach ensures the stabilization of USVs within a guaranteed error bound. Furthermore, a resilient event-triggered strategy is developed to maintain control performance under denial-of-service (DoS) attacks. Additionally, a rigorous stability analysis is conducted for USVs with unknown dynamics, specifying stabilization conditions of durations and frequencies of non-structured DoS attacks. Simulation results, including Monte Carlo tests, demonstrate the effectiveness of the proposed approach, highlighting its robustness and efficiency compared to time-triggered and non-learning-based methods.

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AAMAS Conference 2024 Conference Paper

Whom to Trust? Elective Learning for Distributed Gaussian Process Regression

Zewen Yang
Xiaobing Dai
Akshat Dubey
Sandra Hirche
Georges Hattab

This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, namely prioraware elective distributed GP (Pri-GP), which empowers agents with the capability to selectively request predictions from neighboring agents based on their trustworthiness. The proposed Pri-GP effectively improves individual prediction accuracy, especially in cases where the prior knowledge of an agent is incorrect. Moreover, it eliminates the need for computationally intensive variance calculations for determining aggregation weights in distributed GP. Furthermore, we establish a prediction error bound within the Pri-GP framework, ensuring the reliability of predictions, which is regarded as a crucial property in safety-critical MAS applications.

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