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Zhen Yao

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5 papers

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TCS Journal 2025 Journal Article

Online multi-dimensional generalized assignment problem with predictions

Yimeng Xu
Jiaqi Zheng
Guihai Chen
Xia Zhu
Zhen Yao

The Online Multi-Dimensional Generalized Assignment Problem (online MDGAP) can model a large number of applications such as parallel machine scheduling, vehicle routing, telecommunication network design, etc. , where a set of jobs have to be assigned to a set of capacitated agents in an online manner such that multi-dimensional capacity constraints can be respected. In this paper, we initiate the study of online MDGAP with predictions — the decision parameters such as coefficients of service (switching) costs and resource consumption can be accurately predicted or error-bounded, with the objective of minimizing the sum of service costs and switching costs in the long run. Furthermore, we design a two-stage online algorithm with performance guarantees. Rigorous theoretical analysis in terms of competitive ratio and regret demonstrates that our algorithm can produce an integer solution in polynomial time with bounded dimension constraints violation, robust to the coefficient variations and resource consumption uncertainty. Finally, trace-driven simulations show that our algorithm can achieve near optimal, high utilization, low constraint violation, and strong robustness.

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NeurIPS Conference 2024 Conference Paper

Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments

Feng Xie
Zhen Yao
Lin Xie
Yan Zeng
Zhi Geng

We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper valid instrumental variables (IVs) for the target causal effect by expert knowledge or by assuming that the causal model is a one-directional MR model. As such, in this paper, we first theoretically investigate the identification of the bi-directional MR from observational data. In particular, we provide necessary and sufficient conditions under which valid IV sets are correctly identified such that the bi-directional MR model is identifiable, including the causal directions of a pair of phenotypes (i. e. , the treatment and outcome). Moreover, based on the identification theory, we develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest. We theoretically demonstrate the correctness of the proposed algorithm. Experimental results show the effectiveness of our method for estimating causal effects in both one-directional and bi-directional MR models.

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AAAI Conference 2023 Conference Paper

Analogical Inference Enhanced Knowledge Graph Embedding

Zhen Yao
Wen Zhang
Mingyang Chen
Yufeng Huang
Yi Yang
Huajun Chen

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

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AAAI Conference 2023 Conference Paper

Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

Mingyang Chen
Wen Zhang
Zhen Yao
Yushan Zhu
Yang Gao
Jeff Z. Pan
Huajun Chen

We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vector spaces by assigning them one or multiple specific embeddings (i.e., vector representations). Thus the number of embedding parameters increases linearly as the growth of knowledge graphs. In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities. To obtain the embeddings for the full set of entities, we encode their distinguishable information from their connected relations, k-nearest reserved entities, and multi-hop neighbors. We learn universal and entity-agnostic encoders for transforming distinguishable information into entity embeddings. This approach allows our proposed EARL to have a static, efficient, and lower parameter count than conventional knowledge graph embedding methods. Experimental results show that EARL uses fewer parameters and performs better on link prediction tasks than baselines, reflecting its parameter efficiency.

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IJCAI Conference 2022 Conference Paper

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

Mingyang Chen
Wen Zhang
Zhen Yao
Xiangnan Chen
Mengxiao Ding
Fei Huang
Huajun Chen

We study the knowledge extrapolation problem to embed new components (i. e. , entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.

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