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Huayu Li

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9 papers

2 author rows

JBHI Journal 2026 Journal Article

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features

Huayu Li
Ana S. Carreon-Rascon
Xiwen Chen
Geng Yuan
Ao Li

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach to address these challenges, eliminating the need for extensive human annotation. In this study, we introduce a novel framework for Medical Time Series Representation Learning, known as MTS-LOF. MTS-LOF leverages the strengths of Joint-Embedding SSL and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data. By combining these techniques, MTS-LOF enhances the potential of healthcare applications by providing more sophisticated, context-rich representations. Additionally, MTS-LOF employs a multi-masking strategy to facilitate occlusion-invariant feature learning. This approach allows the model to create multiple views of the data by masking portions of it. By minimizing the discrepancy between the representations of these masked patches and the fully visible patches, MTS-LOF learns to capture rich contextual information within medical time series datasets. The results of experiments conducted on diverse medical time series datasets demonstrate the superiority of MTS-LOF over other methods. These findings hold promise for significantly enhancing healthcare applications by improving representation learning. Furthermore, our work delves into the integration of Joint-Embedding SSL and MAE techniques, shedding light on the intricate interplay between temporal and structural dependencies in healthcare data. This understanding is crucial, as it allows us to grasp the complexities of healthcare data analysis.

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ICML Conference 2025 Conference Paper

FIC-TSC: Learning Time Series Classification with Fisher Information Constraint

Xiwen Chen
Wenhui Zhu
Peijie Qiu
Hao Wang 0176
Huayu Li
Zihan Li
Yalin Wang 0001
Aristeidis Sotiras

Analyzing time series data is crucial to a wide spectrum of applications, including economics, online marketplaces, and human healthcare. In particular, time series classification plays an indispensable role in segmenting different phases in stock markets, predicting customer behavior, and classifying worker actions and engagement levels. These aspects contribute significantly to the advancement of automated decision-making and system optimization in real-world applications. However, there is a large consensus that time series data often suffers from domain shifts between training and test sets, which dramatically degrades the classification performance. Despite the success of (reversible) instance normalization in handling the domain shifts for time series regression tasks, its performance in classification is unsatisfactory. In this paper, we propose $\textit{FIC-TSC}$, a training framework for time series classification that leverages Fisher information as the constraint. We theoretically and empirically show this is an efficient and effective solution to guide the model converges toward flatter minima, which enhances its generalizability to distribution shifts. We rigorously evaluate our method on 30 UEA multivariate and 85 UCR univariate datasets. Our empirical results demonstrate the superiority of the proposed method over 14 recent state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Sequence Complementor: Complementing Transformers for Time Series Forecasting with Learnable Sequences

Xiwen Chen
Peijie Qiu
Wenhui Zhu
Huayu Li
Hao Wang
Aristeidis Sotiras
Yalin Wang
Abolfazl Razi

Since its introduction, the transformer has shifted the development trajectory away from traditional models (e.g., RNN, MLP) in time series forecasting, which is attributed to its ability to capture global dependencies within temporal tokens. Follow-up studies have largely involved altering the tokenization and self-attention modules to better adapt Transformers for addressing special challenges like non-stationarity, channel-wise dependency, and variable correlation in time series. However, we found that the expressive capability of sequence representation is a key factor influencing Transformer performance in time forecasting after investigating several representative methods, where there is an almost linear relationship between sequence representation entropy and mean square error, with more diverse representations performing better. In this paper, we propose a novel attention mechanism with Sequence Complementors and prove feasible from an information theory perspective, where these learnable sequences are able to provide complementary information beyond current input to feed attention. We further enhance the Sequence Complementors via a diversification loss that is theoretically covered. The empirical evaluation of both long-term and short-term forecasting has confirmed its superiority over the recent state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Exploration

Zijian Wang
Bin Wang
Haifeng Jing
Huayu Li
Hongbo Dou

Recent years, multi-hop reasoning has been widely studied for knowledge graph (KG) reasoning due to its efficacy and interpretability. However, previous multi-hop reasoning approaches are subject to two primary shortcomings. First, agents struggle to learn effective and robust policies at the early phase due to sparse rewards. Second, these approaches often falter on specific datasets like sparse knowledge graphs, where agents are required to traverse lengthy reasoning paths. To address these problems, we propose a multi-hop reasoning model with dual agents based on hierarchical reinforcement learning (HRL), which is named FULORA. FULORA tackles the above reasoning challenges by eFficient GUidance-ExpLORAtion between dual agents. The high-level agent walks on the simplified knowledge graph to provide stage-wise hints for the low-level agent walking on the original knowledge graph. In this framework, the low-level agent optimizes a value function that balances two objectives: (1) maximizing return, and (2) integrating efficient guidance from the high-level agent. Experiments conducted on three real-word knowledge graph datasets demonstrate that FULORA outperforms RL-based baselines, especially in the case of long-distance reasoning.

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JBHI Journal 2024 Journal Article

DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal

Huayu Li
Gregory Ditzler
Janet Roveda
Ao Li

Objective: Electrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline wander and noise removal technology. Methods: We extended the diffusion model in a conditional manner that was specific to the ECG signals, namely the Deep Score-Based Diffusion model for Electrocardiogram baseline wander and noise removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy that improved signal reconstructions. We conducted the experiments on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of the proposed method. Baseline methods are adopted for comparison, including traditional digital filter-based and deep learning-based methods. Results: The quantities evaluation results show that the proposed method obtained outstanding performance on four distance-based similarity metrics with at least 20% overall improvement compared with the best baseline method. Conclusion: This paper demonstrates the state-of-the-art performance of the DeScoD-ECG for ECG baseline wander and noise removal, which has better approximations of the true data distribution and higher stability under extreme noise corruptions. Significance: This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.

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ICML Conference 2024 Conference Paper

TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning

Xiwen Chen
Peijie Qiu
Wenhui Zhu
Huayu Li
Hao Wang 0176
Aristeidis Sotiras
Yalin Wang 0001
Abolfazl Razi

Deep neural networks, including transformers and convolutional neural networks (CNNs), have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the sparsity and locality of patterns in time series data (e. g. , quantification of diseases-related anomalous points in ECG and abnormal detection in signal). To address this challenge, we formally discuss and reformulate MTSC as a weakly supervised problem, introducing a novel multiple-instance learning (MIL) framework for better localization of patterns of interest and modeling time dependencies within time series. Our novel approach, TimeMIL, formulates the temporal correlation and ordering within a time-aware MIL pooling, leveraging a tokenized transformer with a specialized learnable wavelet positional token. The proposed method surpassed 26 recent state-of-the-art MTSC methods, underscoring the effectiveness of the weakly supervised TimeMIL in MTSC. The code is available https: //github. com/xiwenc1/TimeMIL.

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IJCAI Conference 2017 Conference Paper

Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach

Huayu Li
Yong Ge
Defu Lian
Hao Liu

Point-of-Interest (POI) recommendation has been an important service on location-based social networks. However, it is very challenging to generate accurate recommendations due to the complex nature of user's interest in POI and the data sparseness. In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user's interest, and adaptively model the missing data. Specifically, a user's general interest in POI is modeled as a mixture of her intrinsic and extrinsic interests, upon which we formulate the ranking constraints in our unified recommendation approach. Furthermore, a self-adaptive location-oriented method is proposed to capture the inherent property of missing data, which is formulated as squared error based loss in our unified optimization objective. Extensive experiments on real-world datasets demonstrate the effectiveness and advantage of our approach.

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IJCAI Conference 2016 Conference Paper

A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback

Huayu Li
Richang Hong
Defu Lian
Zhiang Wu
Meng Wang
Yong Ge

Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that may lead to inaccurate top-k recommendations or are dependent on the sampling of negative feedback that often results in bias. To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data. A smooth and scalable algorithm is developed to solve group- and instance- level's optimization and parameter estimation. Extensive experiments based on real-world datasets demonstrate the effectiveness and advantage of our approach.

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IJCAI Conference 2015 Conference Paper

Regularizing Flat Latent Variables with Hierarchical Structures

Rongcheng Lin
Huayu Li
Xiaojun Quan
Richang Hong
Zhiang Wu
Yong Ge

In this paper, we propose a stratified topic model (STM). Instead of directly modeling and inferring flat topics or hierarchically structured topics, we use the stratified relationships in topic hierarchies to regularize the flat topics. The topic structures are captured by a hierarchical clustering method and play as constraints during the learning process. We propose two theoretically sound and practical inference methods to solve the model. Experimental results with two real world data sets and various evaluation metrics demonstrate the effectiveness of the proposed model.

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