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Zhiyuan Ouyang
Ph.D. researcher in generative modeling, large-scale AI systems, and on-chain execution infrastructure.
Posts
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Blog Post number 1
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portfolio
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publications
Composite-rotating consensus of leaderless multi-agent systems with time-delay
Published in Lecture Notes in Electrical Engineering, 2018
This paper addresses the composite-rotating consensus problem of a class of second order multi-agent systems with time-delay. In order to solve the composite-rotating consensus problems, a distributed control protocol is introduced. Then the stability analysis is completed by using the method of frequency domain analysis and the maximum upper bound of time-delay is also obtained. Finally, the effectiveness of the theoretical results are verified by simulations.
Model detection for grey forecasting model with polynomial term Permalink
Published in Communications in Statistics - Simulation and Computation, 2024
This paper studies grey forecasting models with polynomial terms, covering the traditional grey model, the nonhomogeneous grey model, and integer-order grey models with time-power terms as special cases. It proposes a model detection method for identifying which polynomial orders are truly significant, balancing predictive benefit against added model complexity. Simulations and an empirical study on annual health expenditure in China show that the method can recover the true model structure and deliver strong forecasting performance.
Recommended citation: Zhiyuan Ouyang. "Model detection for grey forecasting model with polynomial term." Communications in Statistics - Simulation and Computation, 2024.
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A novel grey fractional model based on model averaging for forecasting time series
Published in Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2024
The fractional order accumulation has played a crucial role in the development of grey forecasting methods. However, accurately identifying a single fractional order accumulation for modeling diverse sequences is challenging due to the dependence of different fractional order accumulations on data structure over time. To address this issue, we propose a novel fractional grey model abbreviated as FGMMA, incorporating a model averaging method. The new model combines existing fractional grey models by using four judgment criteria, including Akaike information criteria, Bayesian information criteria, Mallows criteria, and Jackknife criteria. Meanwhile, the cutting-edge algorithm named breed particle swarm optimization is employed to search the optimal fractional order for each candidate model to enhance the effectiveness of the designed model. Subsequently, we conduct a Monte Carlo simulation for verification and validation purposes. Finally, empirical analysis based on energy consumption in three countries is conducted to verify the applicability of the proposed model. Compared with other benchmark models, we can conclude that the proposed model outperforms the other competitive models.
A novel network for resolving subjective masking differences and accurate thyroid nodule diagnosis Permalink
Published in Computational Biology and Chemistry , 2025
This paper develops a neural network for thyroid nodule diagnosis that explicitly addresses subjective masking differences in clinical annotations. The method is designed to improve feature extraction robustness and deliver more accurate, reliable predictions for ultrasound-based diagnosis.
Recommended citation: Zhiyuan Ouyang. "A novel network for resolving subjective masking differences and accurate thyroid nodule diagnosis." Computational Biology and Chemistry, 2025.
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Primal-Spectral Generative Modeling: Fast Analytical Generation via Pseudoinverse Levy Inversion Permalink
Published in ICML 2026 (CCF-A), 2026
This paper transforms probability distributions into continuous spectral functions to make generative modeling more amenable to neural approximation, and provides theoretical guarantees for convergence to the true distribution. It introduces PriSpecNet together with a 1-NFE Pseudoinverse Levy Inversion solver that reformulates sampling as a fast analytical problem rather than iterative numerical integration. Experiments show strong improvements on time-series generation and forecasting benchmarks, while achieving competitive ImageNet 256x256 generation with FID 1.66 at dramatically lower computational cost.
Recommended citation: Zhiyuan Ouyang. "Primal-Spectral Generative Modeling: Fast Analytical Generation via Pseudoinverse Levy Inversion." ICML 2026.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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