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Lixing Yu

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4 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Conditional Information Bottleneck-Based Multivariate Time Series Forecasting

Xinhui Li
Liang Duan
Lixing Yu
Kun Yue
Yuehua Li

Multivariate time series (MTS) forecasting endeavors to anticipate the forthcoming sequence of interdependent variables through the utilization of past observations. The prevailing methodologies, relying on deep neural networks, Transformer, or information bottleneck frameworks, persist in confronting challenges such as overlooking or inadequately capturing the inter / intra-series correlations evident in practical MTS datasets. In response to these challenges, we introduce a conditional information bottleneck-based strategy for MTS forecasting, grounded in information theory. Initially, we establish a conditional information bottleneck principle to capture the inter-series correlations via conditioning on non-target variables. Subsequently, a conditional mutual information-based technique is introduced to extract intra-series correlations by conditioning historical data, ensuring temporal consistency within each variable. Lastly, we devise a unified optimization objective and propose a training algorithm to collectively capture inter / intra-series correlations. Empirical investigations on authentic datasets underscore the superiority of our proposed approach over other cutting-edge competitors. Our code is available at https: //github. com/Xinhui-Lee/CIB-MTSF.

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UAI Conference 2025 Conference Paper

Improving Graph Contrastive Learning with Community Structure

Xiang Chen
Kun Yue
Liang Duan
Lixing Yu

Graph contrastive learning (GCL) has demonstrated remarkable success in training graph neural networks (GNNs) by distinguishing positive and negative node pairs without human labeling. However, existing GCL methods often suffer from two limitations: the repetitive message-passing mechanism in GNNs and the quadratic computational complexity of exhaustive node pair sampling in loss function. To address these issues, we propose an efficient and effective GCL framework that leverages community structure rather than relying on the intricate node-to-node adjacency information. Inspired by the concept of sparse low-rank approximation of graph diffusion matrices, our model delivers node messages to the corresponding communities instead of individual neighbors. By exploiting community structures, our method significantly improves GCL efficiency by reducing the number of node pairs needed for contrastive loss calculation. Furthermore, we theoretically prove that our model effectively captures essential structure information for downstream tasks. Extensive experiments conducted on real-world datasets illustrate that our method not only achieves the state-of-the-art performance but also substantially reduces time and memory consumption compared with other GCL methods. Our code is available at [https: //github. com/chenx-hi/IGCL-CS](https: //github. com/chenx-hi/IGCL-CS).

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UAI Conference 2025 Conference Paper

Probabilistic Semantics Guided Discovery of Approximate Functional Dependencies

Liang Duan
Xinran Wu
Xinhui Li
Lixing Yu
Kun Yue

As the general description of relationships between attributes, approximate functional dependencies (AFDs) almost hold for a given dataset with a few violations. Most of existing methods for AFD discover are insufficient to balance the efficiency and accuracy due to the massive search space and permission of violations. To address these issues, we propose an efficient method of probabilistic semantics guided discovery of AFDs based on Bayesian network (BN). Firstly, we learn a BN structure and conduct conditional independence tests on the learned structure rather than the entire search space, such that candidate AFDs could be obtained. Secondly, we fulfill search space reduction and structure pruning by making use of probabilistic semantics of graphical models in terms of BN. Consequently, we provide a branch-and-bound algorithm to discover the AFDs with the highest smoothed mutual information scores. Experimental results illustrate that our proposed method is more effective and efficient than the comparison methods. Our code is available at [https: //github. com/DKE-Code/BNAFD](https: //github. com/DKE-Code/BNAFD).

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IJCAI Conference 2019 Conference Paper

Learning to Learn Gradient Aggregation by Gradient Descent

Jinlong Ji
Xuhui Chen
Qianlong Wang
Lixing Yu
Pan Li

In the big data era, distributed machine learning emerges as an important learning paradigm to mine large volumes of data by taking advantage of distributed computing resources. In this work, motivated by learning to learn, we propose a meta-learning approach to coordinate the learning process in the master-slave type of distributed systems. Specifically, we utilize a recurrent neural network (RNN) in the parameter server (the master) to learn to aggregate the gradients from the workers (the slaves). We design a coordinatewise preprocessing and postprocessing method to make the neural network based aggregator more robust. Besides, to address the fault tolerance, especially the Byzantine attack, in distributed machine learning systems, we propose an RNN aggregator with additional loss information (ARNN) to improve the system resilience. We conduct extensive experiments to demonstrate the effectiveness of the RNN aggregator, and also show that it can be easily generalized and achieve remarkable performance when transferred to other distributed systems. Moreover, under majoritarian Byzantine attacks, the ARNN aggregator outperforms the Krum, the state-of-art fault tolerance aggregation method, by 43. 14%. In addition, our RNN aggregator enables the server to aggregate gradients from variant local models, which significantly improve the scalability of distributed learning.

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