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Yinuo Ren

As seen in: Amazon Science
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Articles

HyperDPO: Hypernetwork-based multi-objective fine-tuning framework

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By Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying
| Amazon Science
In LLM alignment and many other ML applications, one often faces the MultiObjective Fine-Tuning (MOFT) problem, i.e. fine-tuning an existing model with datasets labeled w.r.t. different objectives simultaneously. To address the challenge, we propose the HyperDPO framework, a hypernetwork-based approach that extends the Direct Preference Optimization (DPO) technique, originally developed for efficient LLM alignment with preference data, to accommodate the MOFT settings.
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[2409.16843] Optimal starting point for time series forecasting

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By Yinuo Ren, Feng LI, Haobo Qi, Guangyao Cao
| arxiv.org
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Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity

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By Yinuo Ren, Lexing Ying, Grant M. Rotskoff
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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