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Yicheng Pan

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6 papers

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AAAI Conference 2026 Conference Paper

Hyperbolic Continuous Structural Entropy for Hierarchical Clustering

Guangjie Zeng
Hao Peng
Angsheng Li
Li Sun
Chunyang Liu
Shengze Li
Yicheng Pan
Philip S. Yu

Hierarchical clustering is a fundamental machine-learning technique for grouping data points into dendrograms. However, existing hierarchical clustering methods encounter two primary challenges: 1) Most methods specify dendrograms without a global objective. 2) Graph-based methods often neglect the significance of graph structure, optimizing objectives on complete or static predefined graphs. In this work, we propose Hyperbolic Continuous Structural Entropy neural networks, namely HypCSE, for structure-enhanced continuous hierarchical clustering. Our key idea is to map data points in the hyperbolic space and minimize the relaxed continuous structural entropy (SE) on structure-enhanced graphs. Specifically, we encode graph vertices in hyperbolic space using hyperbolic graph neural networks and minimize approximate SE defined on graph embeddings. To make the SE objective differentiable for optimization, we reformulate it into a function using the lowest common ancestor (LCA) on trees and then relax it into continuous SE (CSE) by the analogy of hyperbolic graph embeddings and partitioning trees. To ensure a graph structure that effectively captures the hierarchy of data points for CSE calculation, we employ a graph structure learning (GSL) strategy that updates the graph structure during training. Extensive experiments on seven datasets demonstrate the superior performance of HypCSE.

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IJCAI Conference 2025 Conference Paper

A Survey of Structural Entropy: Theory, Methods, and Applications

Dingli Su
Hao Peng
Yicheng Pan
Angsheng Li

Classical information theory, a cornerstone of artificial intelligence, is fundamentally limited by its local perspective, often analyzing pairwise interactions while ignoring the larger, hierarchical architecture of complex systems. Structural entropy (SE) presents a paradigm shift, extending Shannon entropy to quantify information on a global scale and measure the uncertainty embedded in a system's organizational hierarchy. Although its applications have broadened significantly from its origins in community detection across diverse AI domains, a systematic synthesis of its theory, computational methods, and applications is currently lacking. This survey provides a comprehensive overview of SE to fill this critical void in the literature. We offer a detailed examination of its theoretical foundations, computational frameworks, and key learning paradigms, with a focus on its integration with graph learning and reinforcement learning. Through an exploration of its diverse applications, we highlight the power of SE to advance graph-based analysis and modeling. Finally, we discuss key challenges and future research opportunities for incorporating SE principles into the development of more interpretable and theoretically grounded AI systems.

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NeurIPS Conference 2025 Conference Paper

Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning

Xianghua Zeng
Hao Peng
Yicheng Pan
Angsheng Li
Guanlin Wu

Diffusion-based generative methods have shown promising potential for modeling trajectories from offline reinforcement learning (RL) datasets, and hierarchical diffusion has been introduced to mitigate variance accumulation and computational challenges in long-horizon planning tasks. However, existing approaches typically assume a fixed two-layer diffusion hierarchy with a single predefined temporal scale, which limits adaptability to diverse downstream tasks and reduces flexibility in decision making. In this work, we propose SIHD, a novel Structural Information-based Hierarchical Diffusion framework for effective and stable offline policy learning in long-horizon environments with sparse rewards. Specifically, we analyze structural information embedded in offline trajectories to construct the diffusion hierarchy adaptively, enabling flexible trajectory modeling across multiple temporal scales. Rather than relying on reward predictions from localized sub-trajectories, we quantify the structural information gain of each state community and use it as a conditioning signal within the corresponding diffusion layer. To reduce overreliance on offline datasets, we introduce a structural entropy regularizer that encourages exploration of underrepresented states while avoiding extrapolation errors from distributional shifts. Extensive evaluations show that SIHD significantly outperforms state-of-the-art baselines in decision-making performance and demonstrates superior generalization across diverse scenarios.

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NeurIPS Conference 2025 Conference Paper

UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems

Tingzhu Bi
Yicheng Pan
Xinrui Jiang
Huize Sun
Meng Ma
Ping Wang

Uncovering cause-effect relationships from observational time series is fundamental to understanding complex systems. While many methods infer static causal graphs, real-world systems often exhibit dynamic causality —where relationships evolve over time. Accurately capturing these temporal dynamics requires time-resolved causal graphs. We propose UnCLe, a novel deep learning method for scalable dynamic causal discovery. UnCLe employs a pair of Uncoupler and Recoupler networks to disentangle input time series into semantic representations and learns inter-variable dependencies via auto-regressive Dependency Matrices. It estimates dynamic causal influences by analyzing datapoint-wise prediction errors induced by temporal perturbations. Extensive experiments demonstrate that UnCLe not only outperforms state-of-the-art baselines on static causal discovery benchmarks but, more importantly, exhibits a unique capability to accurately capture and represent evolving temporal causality in both synthetic and real-world dynamic systems (e. g. , human motion). UnCLe offers a promising approach for revealing the underlying, time-varying mechanisms of complex phenomena.

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IJCAI Conference 2022 Conference Paper

A Simple yet Effective Method for Graph Classification

Junran Wu
Shangzhe Li
Jianhao Li
Yicheng Pan
Ke Xu

In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Intuitively, given a problem, a simpler data structure comes with a simpler algorithm. Here, we investigate the feasibility of improving graph classification performance while simplifying the learning process. Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data. Furthermore, we propose a novel message passing scheme, termed hierarchical reporting, in which features are transferred from leaf nodes to root nodes by following the hierarchical structure of coding trees. We then present a tree kernel and a convolutional network to implement our scheme for graph classification. With the designed message passing scheme, the tree kernel and convolutional network have a lower runtime complexity of O(n) than Weisfeiler-Lehman subtree kernel and other graph neural networks of at least O(hm). We empirically validate our methods with several graph classification benchmarks and demonstrate that they achieve better performance and lower computational consumption than competing approaches.

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TCS Journal 2012 Journal Article

Characterizations of locally testable linear- and affine-invariant families

Angsheng Li
Yicheng Pan

The linear- or affine-invariance is the property of a function family that is closed under linear- or affine-transformations on the domain, and closed under linear combinations of functions, respectively. Both the linear- and affine-invariant families of functions are generalizations of many symmetric families, for instance, the low degree polynomials. Kaufman and Sudan (2007) [21] introduced the notions of “constraint” and “characterization” to characterize the locally testable affine- and linear-invariant families of functions over finite fields of constant size. In this article, it is shown that, for any finite field F of size q and characteristic p, and its arbitrary extension field K of size Q, if an affine-invariant family ℱ ⊆ { K n → F } has a k -local constraint, then it is k ′ -locally testable for k ′ = k 2 Q p Q 2 Q p + 4; and that if a linear-invariant family ℱ ⊆ { K n → F } has a k -local characterization, then it is k ′ -locally testable for k ′ = 2 k 2 Q p Q 4 ( Q p + 1 ). Consequently, for any prime field F of size q, any positive integer k, we have that for any affine-invariant family ℱ over field F, the four notions of “the constraint”, “the characterization”, “the formal characterization” and “the local testability” are equivalent modulo a poly( k, q ) of the corresponding localities; and that for any linear-invariant family, the notions of “the characterization”, “the formal characterization” and “the local testability” are equivalent modulo a poly( k, q ) of the corresponding localities. The results significantly improve, and are in contrast to the characterizations in [21], which have locality exponential in Q, even if the field K is prime. In the research above, a missing result is a characterization of linear-invariant function families by the more natural notion of constraint. For this, we show that a single strong local constraint is sufficient to characterize the local testability of a linear-invariant Boolean function family, and that for any finite field F of size q greater than 2, there exists a linear-invariant function family ℱ over F such that it has a strong 2-local constraint, but is not q d q − 1 − 1 -locally testable. The proof for this result provides an appealing approach toward more negative results in the theme of characterization of locally testable algebraic properties, which is rare, and of course, significant.

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