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Zixuan Yuan

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4 papers

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IJCAI Conference 2024 Conference Paper

R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-channel Information Fusion for Therapy Recommendation

Nengjun Zhu
Jieyun Huang
Jian Cao
Liang Hu
Zixuan Yuan
Huanjing Gao

Integrating data-driven and rule-based approaches is crucial for therapy recommendations since they can collaborate to achieve better performance. Medical rules, which are chains of reasoning that can infer therapies, widely exist. However, their symbolic and logical forms make integrating them with data-driven modeling technologies hard. Although rare attempts have indirectly modeled rules using data that supports them, the poor generalization of medical rules leads to inadequate supporting data and thus impairs the benefit of medical rules. To this end, we propose R2V-MIF, which fills the gap by rule-to-vector contrastive learning (R2V) and multi-channel information fusion (MIF). R2V is a data-free module and utilizes a hypergraph, including condition and result nodes, to instantiate the logic of medical rules. Each rule is reflected in the relations between nodes, and their representations are determined through contrastive learning. By taking rule representations as a bridge, MIF integrates the knowledge from medical rules, similar neighbors, and patient contents, and then recommends therapies. Extensive experiments show that R2V-MIF outperforms the baselines in several metrics using real-world medical data. Our code is available at https: //github. com/vgeek-z/r2vmif.

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AAAI Conference 2024 Conference Paper

Self-Paced Unified Representation Learning for Hierarchical Multi-Label Classification

Zixuan Yuan
Hao Liu
Haoyi Zhou
Denghui Zhang
Xiao Zhang
Hao Wang
Hui Xiong

Hierarchical Multi-Label Classification (HMLC) is a well-established problem that aims at assigning data instances to multiple classes stored in a hierarchical structure. Despite its importance, existing approaches often face two key limitations: (i) They employ dense networks to solely explore the class hierarchy as hard criterion for maintaining taxonomic consistency among predicted classes, yet without leveraging rich semantic relationships between instances and classes; (ii) They struggle to generalize in settings with deep class levels, since the mini-batches uniformly sampled from different levels ignore the varying complexities of data and result in a non-smooth model adaptation to sparse data. To mitigate these issues, we present a Self-Paced Unified Representation (SPUR) learning framework, which focuses on the interplay between instance and classes to flexibly organize the training process of HMLC algorithms. Our framework consists of two lightweight encoders designed to capture the semantics of input features and the topological information of the class hierarchy. These encoders generate unified embeddings of instances and class hierarchy, which enable SPUR to exploit semantic dependencies between them and produce predictions in line with taxonomic constraints. Furthermore, we introduce a dynamic hardness measurement strategy that considers both class hierarchy and instance features to estimate the learning difficulty of each instance. This strategy is achieved by incorporating the propagation loss obtained at each hierarchical level, allowing for a more comprehensive assessment of learning complexity. Extensive experiments on several empirical benchmarks demonstrate the effectiveness and efficiency of SPUR compared to state-of-the-art methods, especially in scenarios with missing features.

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AAAI Conference 2022 Conference Paper

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

Denghui Zhang
Zixuan Yuan
Hao Liu
Xiaodong Lin
Hui Xiong

Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths. However, existing multi-hop reasoning approaches only work well on short reasoning paths and tend to miss the target entity with the increasing path length. This is undesirable for many reasoning tasks in real-world scenarios, where short paths connecting the source and target entities are not available in incomplete KGs, and thus the reasoning performances drop drastically unless the agent is able to seek out more clues from longer paths. To address the above challenge, in this paper, we propose a dual-agent reinforcement learning framework, which trains two agents (GIANT and DWARF) to walk over a KG jointly and search for the answer collaboratively. Our approach tackles the reasoning challenge in long paths by assigning one of the agents (GIANT) searching on cluster-level paths quickly and providing stage-wise hints for another agent (DWARF). Finally, experimental results on several KG reasoning benchmarks show that our approach can search answers more accurately and efficiently, and outperforms existing RL-based methods for long path queries by a large margin.

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AAAI Conference 2021 Conference Paper

Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling

Zixuan Yuan
Hao Liu
Renjun Hu
Denghui Zhang
Hui Xiong

Event-based corporate profiling aims to assess the evolving operational status of the corresponding corporate from its event sequence. Existing studies on corporate profiling have partially addressed the problem via (i) case-by-case empirical analysis by leveraging traditional financial methods, or (ii) the automatic profile inference by reformulating the problem into a supervised learning task. However, both approaches heavily rely on domain knowledge and are laborintensive. More importantly, the task-specific nature of both approaches prevents the obtained corporate profiles from being applied to diversified downstream applications. To this end, in this paper, we propose a Self-Supervised Prototype Representation Learning (SePaL) framework for dynamic corporate profiling. By exploiting the topological information of an event graph and exploring self-supervised learning techniques, SePaL can obtain unified corporate representations that are robust to event noises and can be easily finetuned to benefit various down-stream applications with only a few annotated data. Specifically, we first infer the initial cluster distribution of noise-resistant event prototypes based on latent representations of events. Then, we construct four permutation-invariant self-supervision signals to guide the representation learning of the event prototype. In terms of applications, we exploit the learned time-evolving corporate representations for both stock price spike prediction and corporate default risk evaluation. Experimental results on two real-world corporate event datasets demonstrate the effectiveness of SePaL for these two applications.

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