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Fangfang Li

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Maximizing Schatten-p Norm Regularization Toward Balance

Fangfang Li
Quanxue Gao
Yapeng Wang
Yu Duan
Yuzhuo Feng
Qin Li

The Schatten-p norm, as a class of structure-inducing norms based on singular values, has been widely used to enhance model low-rankness and representation capability due to its flexibility in structural modeling and favorable mathematical properties. However, its potential in cluster distribution modeling has long been overlooked. Therefore, we explore the potential of maximizing the Schatten-p norm as a regularization strategy specifically designed to achieve balanced clustering. This work is the first to investigate its effectiveness in promoting cluster balance. To be specific, maximizing Schatten-p norm effectively guides the assignment of data points, ensuring a more balanced distribution of samples across clusters. We have conducted an in-depth theoretical analysis and validated its effectiveness through extensive clustering experiments. Experimental results demonstrate that, compared to existing methods, this regularization term significantly improves clustering quality and obtain reasonable clustering.

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AAAI Conference 2026 Conference Paper

Unified View Extraction with Low-Rankness and Smoothness Fusion for Multi-View Subspace Clustering

Yapeng Wang
Quanxue Gao
Fangfang Li
Yu Yun
Ming Yang

Tensor-based multi-view subspace clustering (MVSC) has achieved significant success by capturing high-order inter-view correlations. However, existing approaches face two principal limitations. First, most methods either exclusively emphasize the inter-view low‑rankness (R) prior while neglecting the intra-view local smoothness (S) prior, or treat R and S as two separate regularizers—complicating joint optimization. Second, conventional tensor‑based methods impose only low‑rank constraints on the representation tensor, which limits their ability to simultaneously model consistency and complementary information. To address these issues, we propose a Unified View Extraction with Low‑Rankness and Smoothness Fusion (UVELRS) method. Our framework first extracts a consistent cross‑view representation and then constructs a tensor by stacking these representations. We introduce a novel tensor total variation Schatten-p norm that simultaneously encodes both R and S priors while offering flexible singular‑value control. This unified formulation effectively captures both high-order inter-view correlations and intra-view local smoothness. Extensive experiments on real‑world datasets demonstrate UVELRS's superior performance and robustness.

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AAAI Conference 2026 Conference Paper

Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback

Shijing Zhu
Zhuang Chen
Guanqun Bi
Binghang Li
Yaxi Deng
Dazhen Wan
Libiao Peng
Xiyao Xiao

Large language models (LLMs) have shown promise in providing scalable mental health support, while evaluating their counseling capability remains crucial to ensure both efficacy and safety. Existing evaluations are limited by the static assessment that focuses on knowledge tests, the single perspective that centers on user experience, and the open-loop framework that lacks actionable feedback. To address these issues, we propose Ψ-Arena, an interactive framework for comprehensive assessment and optimization of LLM-based counselors, featuring three key characteristics: (1) Realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients; (2) Tripartite evaluation that integrates assessments from the client, supervisor, and counselor perspectives; (3) Closed-loop optimization that iteratively improves LLM counselors using diagnostic feedback. Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives. Moreover, reflection-based optimization results in up to a 141% improvement in counseling performance. We hope Ψ-Arena provides a foundational resource for advancing reliable and human-aligned LLM applications in mental healthcare.

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NeurIPS Conference 2025 Conference Paper

Beyond Node-Centric Modeling: Sketching Signed Networks with Simplicial Complexes

Wei Wu
Xuan Tan
Yan Peng
Ling Chen
Fangfang Li
Chuan Luo

Signed networks can reflect more complex connections through positive and negative edges, and cost-effective signed network sketching can significantly benefit an important link sign prediction task in the era of big data. Existing signed network embedding algorithms mainly learn node representation in the Graph Neural Network (GNN) framework with the balance theory. However, the node-wise representation learning methods either limit the representational power because they primarily rely on node pairwise relationship in the network, or suffer from severe efficiency issues. Recent research has explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by that, we propose EdgeSketch+, a simple and effective edge embedding algorithm beyond traditional node-centric modeling that directly represents edges as low-dimensional vectors without transitioning from node embeddings. The proposed approach maintains a good balance between accuracy and efficiency by exploiting the Locality Sensitive Hashing (LSH) technique to swiftly capture the higher-order information derived from the simplicial complex in a manner of no learning processes. Experiments show that EdgeSketch+ matches state-of-the-art accuracy while significantly reducing runtime, achieving speedups of up to $546. 07\times$ compared to GNN-based methods.

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IROS Conference 2025 Conference Paper

TIETracker: A CLIP-based RGB-T Tracking via Feature Interaction and Semantic Enhancement

Weidai Xia
Xingliang Mao
Wei Wu
Chengzhang Zhu
Fangfang Li

The goal of RGB-T tracking is to enhance the accuracy and robustness by leveraging the complementary features of RGB and TIR modalities in complex scenarios. Previous methods have overlooked the power of semantic features in extracting valuable information from different modalities and improving interactions across them. Moreover, using Bounding Boxes (BBox) for target initialization can cause issues like bounding box blurring and tracking drift when the target’s appearance changes or gets occluded. To address these challenges, we propose the CLIP-based RGBT tracking algorithm TIETracker, which aims to to exploit the complementary advantages of multimodality more effectively using textual information. Textual descriptions direct the backbone network to learn target representations in multimodality and facilitate the interaction of multi-modal features. Additionally, in scenarios of occlusion and scale transformations that lead to missing or altered target features, textual information adaptively supplements the target representation. This approach also improves the response in the image region of the target, addressing issues with bounding box accuracy and tracking drift. Our extensive evaluation on three leading RGB-T tracking benchmarks demonstrates that TIETracker achieves competitive compared to state-of-the-art methods, effectively countering feature loss from changes in target appearance and occlusion.

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AAAI Conference 2020 Short Paper

Distill BERT to Traditional Models in Chinese Machine Reading Comprehension (Student Abstract)

Xingkai Ren
Ronghua Shi
Fangfang Li

Recently, unsupervised representation learning has been extremely successful in the field of natural language processing. More and more pre-trained language models are proposed and achieved the most advanced results especially in machine reading comprehension. However, these proposed pre-trained language models are huge with hundreds of millions of parameters that have to be trained. It is quite time consuming to use them in actual industry. Thus we propose a method that employ a distillation traditional reading comprehension model to simplify the pre-trained language model so that the distillation model has faster reasoning speed and higher inference accuracy in the field of machine reading comprehension. We evaluate our proposed method on the Chinese machine reading comprehension dataset CMRC2018 and greatly improve the accuracy of the original model. To the best of our knowledge, we are the first to propose a method that employ the distillation pretrained language model in Chinese machine reading comprehension.

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