Author name cluster

Shanshan Ye

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.

5 papers

1 author row

TMLR Journal 2026 Journal Article

Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning

Ali Taheri
Alireza Taban
Qizhou Wang
Shanshan Ye
Abdolreza Mirzaei
Tongliang Liu
Bo Han

Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs), notably enhancing their capacity to acquire domain-specific knowledge while preserving or potentially augmenting their general-purpose capabilities. However, the efficacy of SFT hinges on data quality as well as data volume, otherwise it may result in limited performance gains or even degradation relative to the associated baselines. To mitigate such reliance, we suggest categorizing tokens within each corpus into two parts---positive and negative tokens---based on whether they are useful to improve model performance. Positive tokens can be trained in common ways, whereas negative tokens, which may lack essential semantics or be misleading, should be explicitly forgotten. Overall, the token categorization facilitates the model to learn less informative messages, and the forgetting guides the model on what information to learn more precisely. We conduct experiments across diverse and well-established benchmarks using various model architectures, demonstrating that this forgetting mechanism enhances model performance.

PDF Details

AAAI Conference 2026 Conference Paper

PARS: Partial-Label-Learning-inspired Recommender Systems

Shanshan Ye
Kezhi Lu
Guangquan Zhang
Jie Lu

Recommender systems are widely required and deployed to address real-world problems. In this paper, we study a new yet challenging real-world setting for recommender systems, where only user browsing histories are available without any explicit feedback. No item acquisition information, e.g., purchasing or rating, is given. By assuming that user browsing sequences are likely to contain the items to acquire, we draw an analogy to the setting of partial label learning in weakly supervised learning. This enables us to train reliable recommender systems only using browsing histories. We term the proposed method as Partial Acquisition Recommender System (PARS). Empirical results on real-world benchmark datasets show the effectiveness of the proposed method. Surprisingly, we also show that the proposed method even surpasses some baselines using item acquisition information.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation

Maolin Wang
Tianshuo Wei
Sheng Zhang
Ruocheng Guo
Wangyu Wang
Shanshan Ye
Lixin Zou
Xuetao Wei

Neural Architecture Search (NAS) has emerged as a powerful approach for automating neural network design. However, existing NAS methods face critical limitations in real-world deployments: architectures lack adaptability across scenarios, each deployment context requires costly separate searches, and performance consistency across diverse platforms remains challenging. We propose DANCE (Dynamic Architectures with Neural Continuous Evolution), which reformulates architecture search as a continuous evolution problem through learning distributions over architectural components. DANCE introduces three key innovations: a continuous architecture distribution enabling smooth adaptation, a unified architecture space with learned selection gates for efficient sampling, and a multi-stage training strategy for effective deployment optimization. Extensive experiments across five datasets demonstrate DANCE's effectiveness. Our method consistently outperforms state-of-the-art NAS approaches in terms of accuracy while significantly reducing search costs. Under varying computational constraints, DANCE maintains robust performance while smoothly adapting architectures to different hardware requirements. The code and appendix can be found at https: //github. com/Applied-Machine-Learning-Lab/DANCE.

PDF Details DOI

TIST Journal 2025 Journal Article

Robust Learning under Hybrid Noise

Yang Wei
Shuo Chen
Shanshan Ye
Bo Han
Chen Gong

Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both feature noise and label noise, is very common due to the unreliable data collection and annotation processes. Although some results have been achieved by a few representation learning based attempts, this issue is still far from being addressed with promising performance and guaranteed theoretical analyses. To address the challenge, we propose a novel unified learning framework called Feature and Label Recovery (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data. Specifically, the clean feature matrix is discovered by the low-rank approximation, and the ground-truth label matrix is embedded based on the recovered features with a nuclear norm regularization. Meanwhile, the feature noise and label noise are characterized by their respective adaptive matrix norms to satisfy the corresponding maximum likelihood. As this framework leads to a non-convex optimization problem, we develop the non-convex Alternating Direction Method of Multipliers (ADMM) with the convergence guarantee to solve our learning objective. We also provide the theoretical analysis to show that the generalization error of FLR can be upper-bounded in the presence of hybrid noise. Experimental results on several typical benchmark datasets clearly demonstrate the superiority of our proposed method over the state-of-the-art robust learning approaches for various noises.

Details DOI

TIST Journal 2024 Journal Article

Robust Recommender Systems with Rating Flip Noise

Shanshan Ye
Jie Lu

Recommender systems have become important tools in the daily life of human beings since they are powerful to address information overload, and discover relevant and useful items for users. The success of recommender systems largely relies on the interaction history between users and items, which is expected to accurately reflect the preferences of users on items. However, the expectation is easily broken in practice, due to the corruptions made in the interaction history, resulting in unreliable and untrusted recommender systems. Previous works either ignore this issue (assume that the interaction history is precise) or are limited to handling additive noise. Motivated by this, in this paper, we study rating flip noise which widely exists in the interaction history of recommender systems and combat it by modelling the noise generation process. Specifically, the rating flip noise allows a rating to be flipped to any other ratings within the given rating set, which reflects various real-world situations of rating corruption, e.g., a user may randomly click a rating from the rating set and then submit it. The noise generation process is modelled by the noise transition matrix that denotes the probabilities of a clean rating flip into a noisy rating. A statistically consistent algorithm is afterwards applied with the estimated transition matrix to learn a robust recommender system against rating flip noise. Comprehensive experiments on multiple benchmarks confirm the superiority of our method.

Details DOI