Torthaí cuardaigh - Ning, Lin

What Do We Mean by Generalization in Federated Learning? de réir Yuan, Honglin, Morningstar, Warren, Ning, Lin, Singhal, Karan

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MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts de réir Ning, Lin, Lara, Harsh, Guo, Meiqi, Rastogi, Abhinav

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Learning Federated Representations and Recommendations with Limited Negatives de réir Ning, Lin, Singhal, Karan, Zhou, Ellie X., Prakash, Sushant

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Mixed Federated Learning: Joint Decentralized and Centralized Learning de réir Augenstein, Sean, Hard, Andrew, Ning, Lin, Singhal, Karan, Kale, Satyen, Partridge, Kurt, Mathews, Rajiv

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UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches de réir Wang, Chao, Wu, Neo, Ning, Lin, Wu, Jiaxing, Liu, Luyang, Xie, Jun, O'Banion, Shawn, Green, Bradley

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Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning de réir Liu, Yuhan, Ning, Lin, Wu, Neo, Singhal, Karan, Mansfield, Philip Andrew, Berlowitz, Devora, Prakash, Sushant, Green, Bradley

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User-LLM: Efficient LLM Contextualization with User Embeddings de réir Ning, Lin, Liu, Luyang, Wu, Jiaxing, Wu, Neo, Berlowitz, Devora, Prakash, Sushant, Green, Bradley, O'Banion, Shawn, Xie, Jun

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RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs de réir Wu, Jiaxing, Ning, Lin, Liu, Luyang, Lee, Harrison, Wu, Neo, Wang, Chao, Prakash, Sushant, O'Banion, Shawn, Green, Bradley, Xie, Jun

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