21 citations · 63 across the 22 of their papers we have counts for
22 papers
Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruning
Yifan Wu, Yiqi Wang, Xichen Ye +5
Knowledge Distillation (KD) is widely used to obtain compact models for efficient inference in resource-constrained environments. Yet the computational overhead of the distillation…
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering
Yuqing Fu, Yimin Deng, Wanyu Wang +9
Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. It presents two key challenges: generating correct reasoning paths in response to th…
Cognitive Chunking for Soft Prompts: Accelerating Compressor Learning via Block-wise Causal Masking
Guojie Liu, Yiqi Wang, Yanfeng Yang +4
Providing extensive context via prompting is vital for leveraging the capabilities of Large Language Models (LLMs). However, lengthy contexts significantly increase inference laten…
Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
Jiaxin Zhang, Yiqi Wang, Siwei Wang +4
Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, e…
Renormalization Group Guided Tensor Network Structure Search
Maolin Wang, Bowen Yu, Sheng Zhang +8
Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional dat…
BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
Mengyang Ma, Xiaopeng Li, Wanyu Wang +9
Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements al…
Investigating Data Pruning for Pretraining Biological Foundation Models at Scale
Yifan Wu, Jiyue Jiang, Xichen Ye +9
Biological foundation models (BioFMs), pretrained on large-scale biological sequences, have recently shown strong potential in providing meaningful representations for diverse down…
MTA: A Merge-then-Adapt Framework for Personalized Large Language Model
Xiaopeng Li, Yuanjin Zheng, Wanyu Wang +6
Personalized Large Language Models (PLLMs) aim to align model outputs with individual user preferences, a crucial capability for user-centric applications. However, the prevalent a…
Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation
Yi Wen, Yue Liu, Derong Xu +9
Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains. Despite the great su…
Diffusion Generative Recommendation with Continuous Tokens
Haohao Qu, Shanru Lin, Yujuan Ding +2
Recent advances in generative artificial intelligence, particularly large language models (LLMs), have opened new opportunities for enhancing recommender systems (RecSys). Most exi…
Active Negative Loss: A Robust Framework for Learning with Noisy Labels
Xichen Ye, Yifan Wu, Yiqi Wang +3
Deep supervised learning has achieved remarkable success across a wide range of tasks, yet it remains susceptible to overfitting when confronted with noisy labels. To address this…
Mixture of Experts for Node Classification
Yu Shi, Yiqi Wang, WeiXuan Lang +3
Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of no…
Efficient and Robust Regularized Federated Recommendation
Langming Liu, Wanyu Wang, Xiangyu Zhao +9
Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predo…
Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks
Yiqi Wang, Long Yuan, Wenjie Zhang +3
Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large searc…
Dual Test-time Training for Out-of-distribution Recommender System
Xihong Yang, Yiqi Wang, Jin Chen +5
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user…
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang +6
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
Jiaxi Hu, Jingtong Gao, Xiangyu Zhao +6
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential…
Cumulative Distribution Function based General Temporal Point Processes
Maolin Wang, Yu Pan, Zenglin Xu +6
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contribut…
A Comprehensive Survey on Trustworthy Recommender Systems
Wenqi Fan, Xiangyu Zhao, Xiao Chen +8
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalize…
Mixed Graph Contrastive Network for Semi-Supervised Node Classification
Xihong Yang, Yiqi Wang, Yue Liu +5
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…
Localized Graph Collaborative Filtering
Yiqi Wang, Chaozhuo Li, Mingzheng Li +5
User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learni…