Author name cluster

Tingjin Luo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

AAAI Conference 2026 Conference Paper

DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering

Xinyi Wang
Yiping Song
Zhiliang Tian
Bo Liu
Tingjin Luo
Minlie Huang

In multi-hop question answering (MHQA) tasks, Chain of Thought (CoT) improves the quality of generation by guiding large language models (LLMs) through multi-step reasoning, and Knowledge Graphs (KGs) reduce hallucinations via semantic matching. Outcome Reward Models (ORMs) provide feedback after generating the final answers but fail to evaluate the process for multi-step reasoning. Traditional Process Reward Models (PRMs) evaluate the reasoning process but require costly human annotations or rollout generation. While implicit PRM is trained only with outcome signals and derives step rewards through reward parameterization without explicit annotations, it is more suitable for multi-step reasoning in MHQA tasks. However, existing implicit PRM has only been explored for plain text scenarios. When adapting to MHQA tasks, it cannot handle the graph structure constraints in KGs and capture the potential inconsistency between CoT and KG paths. To address these limitations, we propose the DPRM (Dual Implicit Process Reward Model). It trains two implicit PRMs for CoT and KG reasoning in MHQA tasks. Both PRMs, namely KG-PRM and CoT-PRM, derive step-level rewards from outcome signals via reward parameterization without additional explicit annotations. Among them, KG-PRM uses preference pairs to learn structural constraints from KGs. DPRM further introduces a consistency constraint between CoT and KG reasoning steps, making the two PRMs mutually verify and collaboratively optimize the reasoning paths. We also provide a theoretical demonstration of the derivation of process rewards. Experimental results show that our method outperforms 13 baselines on multiple datasets with up to 16.6% improvement on Hit@1.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Multi-Label Classification with Incremental and Decremental Features

Mingdie Jiang
Quanjiang Li
Tingjin Luo
Yiping Song
Chenping Hou

Feature dynamics have emerged as a critical topic about open-environment learning due to the instability of feature availability. While traditional feature evolution targets single-label tasks, multi-label learning is essential to accommodate the exploding annotation spaces. However, multi-label classification with incremental and decremental features is a crucial yet underexplored problem, which poses the challenge of preserving feature representations and label correlations from historical instances and simultaneously adapting to newly arriving streaming data. To address these issues, we propose a two-stage, one-pass learning approach termed MLID. It attempts to compress the informative content of vanished features into the domain of survived ones, facilitate the propagation of label dependencies via low-rank regularization of the classifier, and incorporate augmented features to construct an adaptive classification mechanism. Besides, we design optimization strategies for each stage and provide theoretical guarantees of convergence. Moreover, we establish the generalization error bound of MLID and demonstrate that the compactness of the trace norm and the reuse of models based on effective features can enhance the generalization performance. Finally, we extend it to multi-shot case and extensive experimental results validate the superiority of our MLID.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation

Zhangqi Jiang
Tingjin Luo
Xu Yang
Xinyan Liang

View missing remains a significant challenge in graph-based multi-view semi-supervised learning, hindering their real-world applications. To address this issue, traditional methods introduce a missing indicator matrix and focus on mining partial structure among existing samples in each view for label propagation (LP). However, we argue that these disregarded missing samples sometimes induce discontinuous local structures, i. e. , sub-clusters, breaking the fundamental smoothness assumption in LP. Consequently, such a Sub-Cluster Problem (SCP) would distort graph fusion and degrade classification performance. To alleviate SCP, we propose a novel incomplete multi-view semi-supervised learning method, termed AGF-TI. Firstly, we design an adversarial graph fusion scheme to learn a robust consensus graph against the distorted local structure through a min-max framework. By stacking all similarity matrices into a tensor, we further recover the incomplete structure from the high-order consistency information based on the low-rank tensor learning. Additionally, the anchor-based strategy is incorporated to reduce the computational complexity. An efficient alternative optimization algorithm combining a reduced gradient descent method is developed to solve the formulated objective, with theoretical convergence. Extensive experimental results on various datasets validate the superiority of our proposed AGF-TI as compared to state-of-the-art methods. Code is available at https: //github. com/ZhangqiJiang07/AGF_TI.

PDF Details

AAAI Conference 2025 Conference Paper

Core-to-Global Reasoning for Compositional Visual Question Answering

Hao Zhou
Tingjin Luo
Zhangqi Jiang

Compositional visual question answering (Compositional VQA) needs to provide an answer to a compositional question, which requires the model to have advanced capabilities of multi-modal semantic understanding and logical reasoning. However, current VQA models mainly concentrate on enriching the visual representations of images and neglect the redundancy in the enriched information to bring some negative impacts. To enhance the value and availability of semantic features, we propose a novel core-to-global reasoning (CTGR) model for compositional VQA. The model first extracts both global features and core features from image and question through a feature embedding module. Then, to enhance the value of semantic features, we propose an information filtering module to align visual features and text features at the core semantic level and to filter out the redundancy carried by image and question features at the global semantic level, which can further strengthen cross-modal correlations. Besides, we design a novel core-to-global reasoning mechanism for multimodal fusion, which integrates content features from core learning and context features from global features for accurate answer predictions. Finally, extensive experimental results on GQA, GQA-sub, VQA2.0 and Visual7W demonstrate the effectiveness and superiority of CTGR.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Improving Evolutionary Multi-View Classification via Eliminating Individual Fitness Bias

Xinyan Liang
Shuai Li
Qian Guo
Yuhua Qian
Bingbing Jiang
Tingjin Luo
Liang Du

Evolutionary multi-view classification (EMVC) methods have gained wide recognition due to their adaptive mechanisms. Fitness evaluation (FE), which aims to calculate the classification performance of each individual in the population and provide reliable performance ranking for subsequent operations, is a core step in such methods. Its accuracy directly determines the correctness of the evolutionary direction. That is, when FE fails to correctly reflect the superiority-inferiority relationship among individuals, it will lead to confusion in individual performance ranking, which in turn misleads the evolutionary direction and results in trapping into local optima. This paper is the first to identify the aforementioned issue in the field of EMVC and call it as fitness evaluation bias (FEB). FEB may be caused by a variety of factors, and this paper approaches the issue from the perspective of view information content: existing methods generally adopt joint training strategies, which restrict the exploration of key information in views with low information content. This makes it difficult for multi-view model (MVM) to achieve optimal performance during convergence, which in turn leads to FE failing to accurately reflect individual performance rankings and ultimately triggering FEB. To address this issue, we propose an evolutionary multi-view classification via eliminating individual fitness bias (EFB-EMVC) method, which alleviates the FEB issue by introducing evolutionary navigators for each MVM, thereby providing more accurate individual ranking. Experimental results fully verify the effectiveness of the proposed method in alleviating the FEB problem, and the EMVC method equipped with this strategy exhibits more superior performance compared with the original EMVC method. (The code is available at https: //github. com/LiShuailzn/Neurips-2025-EFB-EMVC)

PDF Details

IJCAI Conference 2025 Conference Paper

One-step Label Shift Adaptation via Robust Weight Estimation

Ruidong Fan
Xiao Ouyang
Tingjin Luo
Lijun Zhang
Chenping Hou

Label shift is a prevalent phenomenon encountered in open environments, characterized by a notable discrepancy in the label distributions between the source (training) and target (test) domains, whereas the conditional distributions given the labels remain invariant. Existing label shift methods adopt a two-step strategy: initially computing the importance weight and subsequently utilizing it to calibrate the target outputs. However, this conventional strategy overlooks the intricate interplay between output adjustment and weight estimation. In this paper, we introduce a novel approach termed as One-step Label Shift Adaptation (OLSA). Our methodology jointly learns the predictive model and the corresponding weights through a bi-level optimization framework, with the objective of minimizing an upper bound on the target risk. To enhance the robustness of our proposed model, we incorporate a debiasing term into the upper-level classifier training and devise a regularization term for the lower-level weight estimation. Furthermore, we present theoretical analyses about the generalization bounds, offering guarantees for the model's performance. Extensive experimental results substantiate the efficacy of our proposal.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Semi-Supervised Multi-View Multi-Label Learning with View-Specific Transformer and Enhanced Pseudo-Label

Quanjiang Li
Tingjin Luo
Mingdie Jiang
Zhangqi Jiang
Chenping Hou
Feijiang Li

Multi-view multi-label learning has become a research focus for describing objects with rich expressions and annotations. However, real-world data often contains numerous unlabeled instances, due to the high cost and technical limitations of manual labeling. This crucial problem involves three main challenges: i) How to extract advanced semantics from available views? ii) How to build a refined classification framework with limited labeled space? iii) How to provide more high-quality supervisory information? To address these problems, we propose a Semi-Supervised Multi-View Multi-Label Learning Method with View-Specific Transformer and Enhanced Pseudo-Label named SMVTEP. Specifically, Generative Adversarial Networks are employed to extract informative shared and specific representations and their consistency and distinctiveness are ensured through the adversarial mechanism and information theory based contrastive learning. Then we build specific classifiers for each extracted feature and apply instance-level manifold constraints to reduce bias across classifiers. Moreover, we design a transformer-style fusion approach that simultaneously captures the imbalance of expressive power among views, mapping effects on specific labels, and label dependencies by incorporating confidence scores and category semantics into the self-attention mechanism. Furthermore, after using Mixup for data augmentation, category-enhanced pseudo-labels are leveraged to improve the reliability of additional annotations by aligning the label distribution of unlabeled samples with the true distribution. Finally, extensive experimental results validate the effectiveness of SMVTEP against state-of-the-art methods.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Theory-Driven Label-Specific Representation for Incomplete Multi-View Multi-Label Learning

Quanjiang Li
Tianxiang Xu
Tingjin Luo
Yan Zhong
Yang Li
Yiyun Zhou
Chenping Hou

Multi-view multi-label learning typically suffers from dual data incompleteness due to limitations in feature storage and annotation costs. The interplay of hetero geneous features, numerous labels, and missing information significantly degrades model performance. To tackle the complex yet highly practical challenges, we propose a Theory-Driven Label-Specific Representation (TDLSR) framework. Through constructing the view-specific sample topology and prototype association graph, we develop the proximity-aware imputation mechanism, while deriving class representatives that capture the label correlation semantics. To obtain semantically distinct view representations, we introduce principles of information shift, inter action and orthogonality, which promotes the disentanglement of representation information, and mitigates message distortion and redundancy. Besides, label semantic-guided feature learning is employed to identify the discriminative shared and specific representations and refine the label preference across views. Moreover, we theoretically investigate the characteristics of representation learning and the generalization performance. Finally, extensive experiments on public datasets and real-world applications validate the effectiveness of TDLSR.

PDF Details

ICML Conference 2025 Conference Paper

Trusted Multi-View Classification with Expert Knowledge Constraints

Xinyan Liang
Shijie Wang
Yuhua Qian
Qian Guo 0005
Liang Du 0003
Bingbing Jiang 0001
Tingjin Luo
Feijiang Li

Multi-view classification (MVC) based on the Dempster-Shafer theory has gained significant recognition for its reliability in safety-critical applications. However, existing methods predominantly focus on providing confidence levels for decision outcomes without explaining the reasoning behind these decisions. Moreover, the reliance on first-order statistical magnitudes of belief masses often inadequately capture the intrinsic uncertainty within the evidence. To address these limitations, we propose a novel framework termed Trusted Multi-view Classification Constrained with Expert Knowledge (TMCEK). TMCEK integrates expert knowledge to enhance feature-level interpretability and introduces a distribution-aware subjective opinion mechanism to derive more reliable and realistic confidence estimates. The theoretical superiority of the proposed uncertainty measure over conventional approaches is rigorously established. Extensive experiments conducted on three multi-view datasets for sleep stage classification demonstrate that TMCEK achieves state-of-the-art performance while offering interpretability at both the feature and decision levels. These results position TMCEK as a robust and interpretable solution for MVC in safety-critical domains. The code is available at https: //github. com/jie019/TMCEK_ICML2025.

Details

IJCAI Conference 2024 Conference Paper

Core-Structures-Guided Multi-Modal Classification Neural Architecture Search

Pinhan Fu
Xinyan Liang
Tingjin Luo
Qian Guo
Yayu Zhang
Yuhua Qian

The multi-modal classification methods based on neural architecture search (NAS-MMC) can automatically learn a satisfied classifier from a given multi-modal search space. However, as the number of multi-modal features and fusion operators increases, the complexity of search space has increased dramatically. Rapidly identifying the satisfied fusion model from this vast space is very challenging. In this paper, we propose an efficient NAS-MMC method based on an idea of shrink-and-expansion search space, called core-structure-guided neural architecture search (CSG-NAS). Specifically, an evolutionary algorithm is first used to find core structures from a shrunk space (also called core structure search space) determined by high-quality features and fusion operators. Then a local search algorithm is used to find the optimal MMC model from the expanded space determined by the discovered core structures and the rest features as well as fusion operators. Moreover, a knowledge transfer strategy is introduced to further improve the overall performance and efficiency of the entire search process. Finally, extensive experimental results demonstrate the effectiveness of our CSG-NAS, attaining the superiority of classification performance, training efficiency and model complexity, compared to state-of-the-art ompetitors on several public benchmark multi-modal tasks. The source code is available at https: //github. com/fupinhan123/CSG-NAS.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Deep Incomplete Multi-View Learning Network with Insufficient Label Information

Zhangqi Jiang
Tingjin Luo
Xinyan Liang

Due to the efficiency of integrating semantic consensus and complementary information across different views, multi-view classification methods have attracted much attention in recent years. However, multi-view data often suffers from both the miss of view features and insufficient label information, which significantly decrease the performance of traditional multi-view classification methods in practice. Learning for such simultaneous lack of feature and label is crucial but rarely studied. To tackle these problems, we propose a novel Deep Incomplete Multi-view Learning Network (DIMvLN) by incorporating graph networks and semi-supervised learning in this paper. Specifically, DIMvLN firstly designs the deep graph networks to effectively recover missing data with assigning pseudo-labels of large amounts of unlabeled instances and refine the incomplete feature information. Meanwhile, to enhance the label information, a novel pseudo-label generation strategy with the similarity constraints of unlabeled instances is proposed to exploit additional supervisory information and guide the completion module to preserve more semantic information of absent multi-view data. Besides, we design view-specific representation extractors with the autoencoder structure and contrastive loss to learn high-level semantic representations for each view, promote cross-view consistencies and augment the separability between different categories. Finally, extensive experimental results demonstrate the effectiveness of our DIMvLN, attaining noteworthy performance improvements compared to state-of-the-art competitors on several public benchmark datasets. Code will be available at GitHub.

PDF Details DOI