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Parameters Efficient Fine-Tuning for Long-Tailed Sequential Recommendation., , , , , and . CICAI (1), volume 14473 of Lecture Notes in Computer Science, page 442-459. Springer, (2023)Learning to Reweight for Graph Neural Network., , , , , , , , and . CoRR, (2023)DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization., , , , , , , , , and 1 other author(s). WWW, page 3077-3085. ACM, (2023)Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives., , , , , , , and . Frontiers Inf. Technol. Electron. Eng., 24 (10): 1390-1402 (October 2023)AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation., , , , , , and . CoRR, (2024)ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation., , , , and . CoRR, (2024)Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer., , , , , , , and . CoRR, (2023)Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling., , , , , and . CoRR, (2022)Learning to Reweight for Generalizable Graph Neural Network., , , , , , , , and . AAAI, page 8320-8328. AAAI Press, (2024)ART: rule bAsed futuRe-inference deducTion., , , , , , , , , and 1 other author(s). EMNLP, page 9512-9522. Association for Computational Linguistics, (2023)