16 citations · 34 across the 17 of their papers we have counts for
13 papers · 1 filter
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
Ruijiang Dong, Zesheng Ye, Jianzhong Qi +4
Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by ins…
Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
Tan-Ha Mai, Chao-Kai Chiang, Han-Hwa Shih +3
Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of research, CLL methods remain…
Understanding Negative Learning in Large Language Model Fine-tuning
Yue Wang, Qizhou Wang, Zizhuo Zhang +3
Negative learning has never been more important than it is today, particularly in large language model (LLM) fine-tuning, where it underpins machine unlearning, preference- and rew…
From Coefficients to Directions: Rethinking Model Merging with Directional Alignment
Zhikang Chen, Sen Cui, Deheng Ye +5
Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Previous studies have demonstr…
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
Yongqiang Chen, Gang Niu, James Cheng +2
Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervis…
Towards Understanding Valuable Preference Data for Large Language Model Alignment
Zizhuo Zhang, Qizhou Wang, Shanshan Ye +4
Large language model (LLM) alignment is typically achieved through learning from human preference comparisons, making the quality of preference data critical to its success. Existi…
Rethinking Consistent Multi-Label Classification Under Inexact Supervision
Wei Wang, Tianhao Ma, Ming-Kun Xie +2
Partial multi-label learning and complementary multi-label learning are two popular weakly supervised multi-label classification paradigms that aim to alleviate the high annotation…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…
Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
Zhiqiang Kou, Si Qin, Hailin Wang +6
Multi-label learning (MLL) has gained attention for its ability to represent real-world data. Label Distribution Learning (LDL), an extension of MLL to learning from label distribu…
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Jia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan +3
Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlik…
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
Qizhou Wang, Bo Han, Puning Yang +3
The imperative to eliminate undesirable data memorization underscores the significance of machine unlearning for large language models (LLMs). Recent research has introduced a seri…
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu +3
Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown…
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Haoang Chi, Feng Liu, Bo Han +6
In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However,…