arXiv subjects

Dongyan Sui

Publications and source records attributed to Dongyan Sui.

4 recordsLinked to original sources

arXiv2026

Alpha-Beta HMM: Interpretable Low-Parameter Hidden Markov Filtering in Dynamic Environments

Practical online inference in dynamic environments requires a lightweight filtering mechanism that remains adaptive to state changes while retaining reliable information from past noisy observations. To address this challenge, we propose the $αβ$-HMM, an interpretable low-parameter hidden Markov filtering framework that replaces the full transition matrix with an equal-exit surrogate governed by an exit-probability parameter $α$, and introduces a step-size parameter $β$ through a generalized measurement update to regulate the influence of observational evidence. A central feature of the proposed method is that it preserves the nonlinear log-belief-ratio dynamics of HMM-type filtering, which turn out to be critical for strong performance. To analyze this nonlinear recursion, we develop a dynamical-systems framework and a deterministic reference system, through which we characterize adaptation capability, learning performance, and practical guidance for selecting the two proposed parameters. In parallel, we study the approximation error induced by the equal-exit surrogate and show that the resulting low-parameter filter remains competitive with the oracle HMM across a broad range of environments. These results reveal an explicit learning-adaptation trade-off induced by the two proposed parameters, provide principled guidance for parameter tuning, and show that strong filtering performance can be achieved within a tractable and interpretable low-parameter framework.

eess.SY↗

arXiv2025

Decentralized Hidden Markov Modeling with Equal Exit Probabilities

Social learning strategies enable agents to infer the underlying true state of nature in a distributed manner by receiving private environmental signals and exchanging beliefs with their neighbors. Previous studies have extensively focused on static environments, where the underlying true state remains unchanged over time. In this paper, we consider a dynamic setting where the true state evolves according to a Markov chain with equal exit probabilities. Based on this assumption, we present a social learning strategy for dynamic environments, termed Diffusion $α$-HMM. By leveraging a simplified parameterization, we derive a nonlinear dynamical system that governs the evolution of the log-belief ratio over time. This formulation further reveals the relationship between the linearized form of Diffusion $α$-HMM and Adaptive Social Learning, a well-established social learning strategy for dynamic environments. Furthermore, we analyze the convergence and fixed-point properties of a reference system, providing theoretical guarantees on the learning performance of the proposed algorithm in dynamic settings. Numerical experiments compare various distributed social learning strategies across different dynamic environments, demonstrating the impact of nonlinearity and parameterization on learning performance in a range of dynamic scenarios.

cs.MA↗

arXiv2024

Non-Bayesian Social Learning with Multiview Observations

Non-Bayesian social learning enables multiple agents to conduct networked signal and information processing through observing environmental signals and information aggregating. Traditional non-Bayesian social learning models only consider single signals, limiting their applications in scenarios where multiple viewpoints of information are available. In this work, we exploit, in the information aggregation step, the independently learned results from observations taken from multiple viewpoints and propose a novel non-Bayesian social learning model for scenarios with multiview observations. We prove the convergence of the model under traditional assumptions and provide convergence conditions for the algorithm in the presence of misleading signals. Through theoretical analyses and numerical experiments, we validate the strong reliability and robustness of the proposed algorithm, showcasing its potential for real-world applications.

cs.SI↗

arXiv2023

UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification

Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural Networks (HGNNs), and their well-designed variants, on some commonly used benchmark graphs and hypergraphs, they are outperformed by even a simple Multi-Layer Perceptron. This observation motivates a reexamination of the design paradigm of the current GNNs and HGNNs and poses challenges of extracting graph features effectively. In this work, a universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder. The architecture starts with a forward transformation of the topological relationships of connected nodes into edge or hyperedge features via a normalized projection matrix. The resulting edge/hyperedge features, together with the original node features, are fed into a neural network. The encoded node embeddings are then derived from the reversed transformation, described by the transpose of the projection matrix, of the network's output, which can be further used for tasks such as node classification. The proposed architecture, in contrast to the traditional spectral-based and/or message passing approaches, simultaneously and comprehensively exploits the node features and graph/hypergraph topologies in an efficient and unified manner, covering both heterophilic and homophilic graphs. The designed projection matrix, encoding the graph features, is intuitive and interpretable. Extensive experiments are conducted and demonstrate the superior performance of the proposed framework on twelve representative hypergraph datasets and six real-world graph datasets, compared to the state-of-the-art methods. Our implementation is available online at https://github.com/MinhZou/UniG-Encoder.

cs.LG↗