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Fei Teng

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Distribution Shift Is Key to Learning Invariant Prediction

Hong Zheng
Fei Teng

An interesting phenomenon arises: Empirical Risk Minimization (ERM) sometimes outperforms methods specifically designed for out-of-distribution tasks. This motivates an investigation into the reasons behind such behavior beyond algorithmic design. In this study, we find that one such reason lies in the distribution shift across training domains. A large degree of distribution shift can lead to better performance even under ERM. Specifically, we derive several theoretical and empirical findings demonstrating that distribution shift plays a crucial role in model learning and benefits learning invariant prediction. Firstly, the proposed upper bounds indicate that the degree of distribution shift directly affects the prediction ability of the learned models. If it is large, the models’ ability can increase, approximating invariant prediction models that make stable predictions under arbitrary known or unseen domains; and vice versa. We also prove that, under certain data conditions, ERM solutions can achieve performance comparable to that of invariant prediction models. Secondly, the empirical validation results demonstrated that the predictions of learned models approximate those of Oracle or Optimal models, provided that the degree of distribution shift in the training data increases.

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EAAI Journal 2026 Journal Article

High-precision multimodal vehicle trajectory prediction model based on cross-layer interleaved spatiotemporal attention mechanism

Fei Teng
Liqiang Jin
Junnian Wang
Feng Xiao
Mengdi Guo
Yanbo Zhou
Jin Zhang

In increasingly complex traffic environments, spatiotemporal attention mechanisms have made remarkable advancements in scene-level interaction modelling. However, the deep and multi-scale spatiotemporal representations required for safe and efficient decision-making in intelligent vehicles remain underexplored. Aiming to address this limitation, this study proposes a multimodal trajectory prediction model based on a cross-layer interleaved spatiotemporal attention (CLISTA) mechanism. Compared with conventional spatiotemporal attention frameworks, CLISTA more effectively captures multi-scale spatiotemporal interactions in complex traffic scenes through the alternating fusion of spatial and temporal features across network layers via a cross-layer interleaving structure. Firstly, spatial, dynamic and heading conflict risks are derived from the relative motion between the target vehicle and its neighbours and aggregated into a social grid weight matrix, through which the neighbours' collective influence on the target vehicle is quantified. Secondly, spatial and temporal multi-head attention modules are designed within each layer. By integrating an interleaved ‘spatial–temporal’ stacking strategy with cross-layer skip connections, the model facilitates progressive alignment and deep fusion, ranging from local interactions to long-range dependencies. Subsequently, an intention recognition module is developed. A second-order gated bilinear fusion mechanism is introduced to adaptively model higher-order couplings between local neighbour dynamics and global interaction semantics, thereby yielding a multimodal probability distribution over the target vehicle's driving intentions. Lastly, multimodal trajectory predictions are generated by decoding the fused spatiotemporal features together with the inferred intention information. Experimental results on three benchmark datasets—NGSIM (Next Generation Simulation), AD4CHE (Aerial Dataset for China Congested Highway and Expressway), and highD—demonstrate that CLISTA consistently outperforms the baseline methods. Relative to the next-best model, it reduces average/final displacement errors by 16. 67 %/21. 23 %, 12. 99 %/21. 14 % and 10. 53 %/21. 59 % on NGSIM, AD4CHE and HighD, respectively. Overall, CLISTA offers reliable multi-hypothesis trajectory priors for safe and efficient decision-making in complex traffic scenarios.

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AAAI Conference 2026 Conference Paper

Uncovering Hidden Degeneration: A Physics-Guided Bidirectional Inference Framework for Industrial Time Series Prediction

Xingwang Li
Fei Teng
Xin Wu
Qiang Duan

Hidden degenerations in industrial time series often precede observable failures, they remain undetected by standard monitoring systems until anomalies become apparent. This gap between microscopic degradation and macroscopic observation renders conventional predictors inherently reactive, as they rely on correlations in sensor data rather than uncovering the underlying, physics‑consistent degradation states. Crucially, the microscopic mechanisms governing system evolution depend on macroscopic state variables—whose measurements are expectations over microscopic probability distributions—so purely data‑driven “top‑down” or purely physics‑guided “bottom‑up” approaches cannot forecast degeneration‑entangled industrial faults. To address these challenges, we propose a Physics-Guided Bidirectional Inference Framework that represents hidden microscopic states from macroscopic measurements. Our approach uniquely combines: (1) bottom-up physics-based simulation using Continuum Damage Mechanics to model micro-scale damage evolution under environmental stressors, and (2) top-down probabilistic inference via maximum entropy formalism to estimate latent microstate distributions from sparse sensor data. This bidirectional mechanism enables early failure prediction by bridging observable measurements with unobservable degeneration. Validation on real-world railway infrastruc datasets demonstrates significant improvements in early fault prediction compared to state-of-the-art baselines. Our method establishes a new paradigm for safety-critical industrial applications requiring reliable prediction of hidden degeneration processes.

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EAAI Journal 2025 Journal Article

Self-supervised contrastive learning for implicit collaborative filtering

Shipeng Song
Bin Liu
Fei Teng
Tianrui Li

Recommendation systems are a critical application of artificial intelligence (AI), driving personalized user experiences across various platforms. Recent advancements in contrastive learning-based recommendation algorithms have led to significant progress in self-supervised recommendation. A key method in this field is Bayesian Personalized Ranking (BPR), which has become a dominant approach for implicit collaborative filtering. However, the challenge of false-positive and false-negative examples in implicit feedback continues to hinder accurate preference learning. In this study, we introduce an efficient self-supervised contrastive learning framework that enhances the supervisory signal by incorporating positive feature augmentation and negative label augmentation. Our theoretical analysis reveals that this approach is equivalent to maximizing the likelihood estimation with latent variables representing user interest centers. Additionally, we present a novel negative label augmentation technique that selects unlabeled examples based on their relative ranking positions, enabling efficient augmentation with constant time complexity. Validation on the MovieLens-100k, MovieLens-1M, Yahoo! -R3, Yelp2018, and Gowalla datasets demonstrates that our method achieves over a 5% improvement in precision compared to the widely used BPR optimization objective, while maintaining comparable runtime efficiency.

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IROS Conference 2025 Conference Paper

Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance

Jiayi Zhao
Fei Teng
Kai Luo
Guoqiang Zhao
Zhiyong Li 0001
Xu Zheng 0002
Kailun Yang 0001

The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong perception potential in perception tasks, its inherent training paradigm prevents it from being suitable for RGB-T tasks. To address these challenges, we propose SHIFNet, a novel SAM2-driven Hybrid Interaction Paradigm that unlocks the potential of SAM2 with linguistic guidance for efficient RGB-Thermal perception. Our framework consists of two key components: (1) Semantic-Aware Cross-modal Fusion (SACF) module that dynamically balances modality contributions through text-guided affinity learning, overcoming SAM2’s inherent RGB bias; (2) Heterogeneous Prompting Decoder (HPD) that enhances global semantic information through a semantic enhancement module and then combined with category embeddings to amplify cross-modal semantic consistency. With 32. 27M trainable parameters, SHIFNet achieves state-of-the-art segmentation performance on public benchmarks, reaching 89. 8% on PST900 and 67. 8% on FMB, respectively. The framework facilitates the adaptation of pre-trained large models to RGB-T segmentation tasks, effectively mitigating the high costs associated with data collection while endowing robotic systems with comprehensive perception capabilities. The source code will be made publicly available at https://github.com/iAsakiT3T/SHIFNet.

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AAAI Conference 2024 Conference Paper

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning

Wangkun Xu
Jianhong Wang
Fei Teng

Successful machine learning involves a complete pipeline of data, model, and downstream applications. Instead of treating them separately, there has been a prominent increase of attention within the constrained optimization (CO) and machine learning (ML) communities towards combining prediction and optimization models. The so-called end-to-end (E2E) learning captures the task-based objective for which they will be used for decision making. Although a large variety of E2E algorithms have been presented, it has not been fully investigated how to systematically address uncertainties involved in such models. Most of the existing work considers the uncertainties of ML in the input space and improves robustness through adversarial training. We extend this idea to E2E learning and prove that there is a robustness certification procedure by solving augmented integer programming. Furthermore, we show that neglecting the uncertainty of COs during training causes a new trigger for generalization errors. To include all these components, we propose a unified framework that covers the uncertainties emerging in both the input feature space of the ML models and the COs. The framework is described as a robust optimization problem and is practically solved via end-to-end adversarial training (E2E-AT). Finally, the performance of E2E-AT is evaluated by a real-world end-to-end power system operation problem, including load forecasting and sequential scheduling tasks.

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AAAI Conference 2023 Conference Paper

AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems

Xu Wan
Mingyang Sun
Boli Chen
Zhongda Chu
Fei Teng

With the increasing penetration of inverter-based renewable energy resources, deep reinforcement learning (DRL) has been proposed as one of the most promising solutions to realize real-time and autonomous control for future carbon-neutral power systems. In particular, DRL-based frequency control approaches have been extensively investigated to overcome the limitations of model-based approaches, such as the computational cost and scalability for large-scale systems. Nevertheless, the real-world implementation of DRLbased frequency control methods is facing the following fundamental challenges: 1) safety guarantee during the learning and decision-making processes; 2) adaptability against the dynamic system operating conditions. To this end, this is the first work that proposes an Adaptive and Safe-Certified DRL (AdapSafe) algorithm for frequency control to simultaneously address the aforementioned challenges. In particular, a novel self-tuning control barrier function is designed to actively compensate the unsafe frequency control strategies under variational safety constraints and thus achieve guaranteed safety. Furthermore, the concept of meta-reinforcement learning is integrated to significantly enhance its adaptiveness in non-stationary power system environments without sacrificing the safety cost. Experiments are conducted based on GB 2030 power system, and the results demonstrate that the proposed AdapSafe exhibits superior performance in terms of its guaranteed safety in both training and test phases, as well as its considerable adaptability against the dynamics changes of system parameters.

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JBHI Journal 2020 Journal Article

Automatic Medical Code Assignment via Deep Learning Approach for Intelligent Healthcare

Fei Teng
Zheng Ma
Jie Chen
Ming Xiao
Lufei Huang

With the development of healthcare 4. 0, there has been an explosion in the amount of data such as image, medical text, physiological signals, lab tests, etc. Among them, medical records provide a complete picture of the associated clinical events. However, the processing of medical texts is difficult because they are structurally free, diverse in style, and have subjective factors. Assigning metadata codes from the International Classification of Diseases (ICD) presents a standardized way of indicating diagnoses and procedures, so it becomes a mandatory process for understanding medical records to make better clinical and financial decisions. Such a manual encoding task is time-consuming, error-prone and expensive. In this paper, we proposed a deep learning approach and a medical topic mining method to automatically predict ICD codes from text-free medical records. The result of the F1 score on Medical Information Mart for Intensive Care (MIMIC-III) dataset increases by 5% over the state of art. It also suitable for multiple ICD versions and languages. For the specific disease, atrial fibrillation, the F1 score is up to 96% and 93. 3% using in-house ICD-10 datasets and MIMIC-III datasets, respectively. We developed an Artificial Intelligence based coding system, which can greatly improve the efficiency and accuracy of human coders, and meanwhile accelerate the secondary use for clinical informatics.

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JBHI Journal 2020 Journal Article

Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network

Qiang Xu
Yu Zeng
Wenjun Tang
Wei Peng
Tingwei Xia
Zongrun Li
Fei Teng
Weihong Li

Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception.

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IJCAI Conference 2020 Conference Paper

RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task

Yu Zeng
Yan Gao
Jiaqi Guo
Bei Chen
Qian Liu
Jian-Guang Lou
Fei Teng
Dongmei Zhang

Neural semantic parsers usually fail to parse long and complicated utterances into nested SQL queries, due to the large search space. In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation problem into several progressive non-nested SQL query generation problems. Furthermore, we propose a novel Question Decomposer module to explicitly encourage RECPARSER to focus on different components of an utterance when predicting SQL queries of different layers. Experiments on the Spider dataset show that our approach is more effective compared to the previous works at predicting the nested SQL queries. In addition, we achieve an overall accuracy that is comparable with state-of-the-art approaches.

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