:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Quan, Yu, Wenchao, Wang, Suhang, Lin, Minhua, Chen, Lingwei, Cheng, Wei, Chen, Haifeng
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.20651
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
by: Li, Quan, et al.
Published: (2024)

Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference
by: Li, Quan, et al.
Published: (2024)

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
by: Lee, Geon, et al.
Published: (2025)

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
by: Wang, Fali, et al.
Published: (2024)

LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
by: Xu, Junjie, et al.
Published: (2024)

PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection
by: Dai, Enyan, et al.
Published: (2024)

Interpretable Imitation Learning with Dynamic Causal Relations
by: Zhao, Tianxiang, et al.
Published: (2023)

TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
by: Jiang, Yushan, et al.
Published: (2025)

Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
by: Zhang, Zhiwei, et al.
Published: (2024)

Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
by: Liu, Chen, et al.
Published: (2024)

Consistently Informative Soft-Label Temperature for Knowledge Distillation
by: Luong, Hoang-Chau, et al.
Published: (2026)

Rethinking the Role of Temperature in Large Language Model Distillation
by: Luong, Hoang-Chau, et al.
Published: (2026)

Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
by: Shen, ChengAo, et al.
Published: (2025)

Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)

Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
by: Wang, Muyao, et al.
Published: (2024)

Stealing Training Graphs from Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)

Robustness Inspired Graph Backdoor Defense
by: Zhang, Zhiwei, et al.
Published: (2024)

Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
by: Chen, Wenjie, et al.
Published: (2025)

Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation
by: Luong, Hoang-Chau, et al.
Published: (2026)

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
by: Li, Hui, et al.
Published: (2025)

Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)

DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models
by: Huang, Yongqi, et al.
Published: (2025)

E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
by: Jiang, Lin, et al.
Published: (2026)

Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model
by: Chen, Jinyin, et al.
Published: (2024)

Graph Knowledge Distillation to Mixture of Experts
by: Rumiantsev, Pavel, et al.
Published: (2024)

Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance
by: Li, Jihang, et al.
Published: (2026)

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction
by: Ye, Liliang, et al.
Published: (2025)

Extreme Region Policy Distillation
by: Chen, Changyu, et al.
Published: (2026)

ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting
by: Li, Wei
Published: (2026)

Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)
by: Zhang, Likun, et al.
Published: (2025)

Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
by: Zeng, Cong, et al.
Published: (2025)

Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation
by: Su, Ye, et al.
Published: (2025)

SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search
by: Li, Dong, et al.
Published: (2025)

POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
by: Wang, Junxiang, et al.
Published: (2023)

ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning
by: Shi, Jimeng, et al.
Published: (2024)

Spatial-Temporal Knowledge Distillation for Takeaway Recommendation
by: Zhao, Shuyuan, et al.
Published: (2024)

Federated Learning with Extremely Noisy Clients via Negative Distillation
by: Lu, Yang, et al.
Published: (2023)

Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer
by: Zhu, Lingwei, et al.
Published: (2025)

TimeExpert: Boosting Long Time Series Forecasting with Temporal Mix of Experts
by: Ma, Xiaowen, et al.
Published: (2025)

Photonic Quantum-Enhanced Knowledge Distillation
by: Chen, Kuan-Cheng, et al.
Published: (2026)