437 citations · 814 across the 15 of their papers we have counts for
15 papers
Panda: Test-Time Adaptation with Negative Data Augmentation
Ruxi Deng, Wenxuan Bao, Tianxin Wei +1
Pretrained VLMs exhibit strong zero-shot classification capabilities, but their predictions degrade significantly under common image corruptions. To improve robustness, many test-t…
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Tianxin Wei, Noveen Sachdeva, Benjamin Coleman +12
Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and ev…
Graph4MM: Weaving Multimodal Learning with Structural Information
Xuying Ning, Dongqi Fu, Tianxin Wei +2
Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in…
Connecting Domains and Contrasting Samples: A Ladder for Domain Generalization
Tianxin Wei, Yifan Chen, Xinrui He +2
Distribution shifts between training and testing samples frequently occur in practice and impede model generalization performance. This crucial challenge thereby motivates studies…
NIRVANA: Structured Pruning Reimagined for Large Language Model Compression
Mengting Ai, Tianxin Wei, Sirui Chen +1
While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance…
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
Xinrui He, Yikun Ban, Jiaru Zou +3
Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (L…
Meta Clustering of Neural Bandits
Yikun Ban, Yunzhe Qi, Tianxin Wei +2
The contextual bandit has been identified as a powerful framework to formulate the recommendation process as a sequential decision-making process, where each item is regarded as an…
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
Xinrui He, Tianxin Wei, Jingrui He
The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of it…
Adaptive Test-Time Personalization for Federated Learning
Wenxuan Bao, Tianxin Wei, Haohan Wang +1
Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts. However, most of these methods require labeled data on te…
MLP Fusion: Towards Efficient Fine-tuning of Dense and Mixture-of-Experts Language Models
Mengting Ai, Tianxin Wei, Yifan Chen +2
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, this process is known to be expensive,…
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei, Yuning You, Tianlong Chen +3
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (w…
Comprehensive Fair Meta-learned Recommender System
Tianxin Wei, Jingrui He
In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, man…
Neural Collaborative Filtering Bandits via Meta Learning
Yikun Ban, Yunzhe Qi, Tianxin Wei +1
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.…
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng, Xiangnan He +4
Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method pers…
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Tianxin Wei, Fuli Feng, Jiawei Chen +3
The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e.…