20 citations · 23 across the 15 of their papers we have counts for
17 papers
Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees
Zhongnian Li, Shi Xu, Mengting Xu +2
Reflective language agents reuse feedback and memory to adapt across tasks without updating model weights. However, existing guarantees for policy selection and action generation d…
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems
Zhongnian Li, Shi Xu, Peng Ying +3
Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting. However, OPRO's optimization pr…
One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors
Zhiwei Zhao, Weikang Gong, Zhongnian Li +1
Accelerated MRI reconstruction recovers images from undersampled k-space. However, different acceleration factors produce distinct artifact patterns. Existing methods often train s…
Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation
Ridong Han, Yawen Shen, Zhongnian Li +3
Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution s…
Human-Corrected Labels Learning: Enhancing Labels Quality via Human Correction of VLMs Discrepancies
Zhongnian Li, Lan Chen, Yixin Xu +2
Vision-Language Models (VLMs), with their powerful content generation capabilities, have been successfully applied to data annotation processes. However, the VLM-generated labels e…
Learning from Uncertain Similarity and Unlabeled Data
Meng Wei, Zhongnian Li, Peng Ying +1
Existing similarity-based weakly supervised learning approaches often rely on precise similarity annotations between data pairs, which may inadvertently expose sensitive label info…
MSGCoOp: Multiple Semantic-Guided Context Optimization for Few-Shot Learning
Zhaolong Wang, Tongfeng Sun, Mingzheng Du +1
Vision-language pre-trained models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, and prompt learning has emerged as an efficient alternative to full fi…
ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
Zhongnian Li, Meng Wei, Peng Ying +1
Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the…
Learning from Concealed Labels
Zhongnian Li, Meng Wei, Peng Ying +2
Annotating data for sensitive labels (e.g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios. To cope with this problem, we propose a…
Seeing the Undefined: Chain-of-Action for Generative Semantic Labels
Meng Wei, Zhongnian Li, Peng Ying +1
Recent advances in vision-language models (VLMs) have demonstrated remarkable capabilities in image classification by leveraging predefined sets of labels to construct text prompts…
Learning from True-False Labels via Multi-modal Prompt Retrieving
Zhongnian Li, Jinghao Xu, Peng Ying +2
Pre-trained Vision-Language Models (VLMs) exhibit strong zero-shot classification abilities, demonstrating great potential for generating weakly supervised labels. Unfortunately, e…
Determined Multi-Label Learning via Similarity-Based Prompt
Meng Wei, Zhongnian Li, Peng Ying +2
In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each tra…
Learning from Reduced Labels for Long-Tailed Data
Meng Wei, Zhongnian Li, Yong Zhou +1
Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and…
Learning from Stochastic Labels
Meng Wei, Zhongnian Li, Yong Zhou +2
Annotating multi-class instances is a crucial task in the field of machine learning. Unfortunately, identifying the correct class label from a long sequence of candidate labels is…
Complementary Labels Learning with Augmented Classes
Zhongnian Li, Jian Zhang, Mengting Xu +2
Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compar…
Class-Imbalanced Complementary-Label Learning via Weighted Loss
Meng Wei, Yong Zhou, Zhongnian Li +1
Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbala…
Learning from Positive and Unlabeled Data with Augmented Classes
Zhongnian Li, Liutao Yang, Zhongchen Ma +3
Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learn…