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Lexing Ying on Muck Rack

Lexing Ying

As seen in: Physical Review B, 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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[2408.04209] Solving high-dimensional Hamilton-Jacobi-Bellman equation with functional hierarchical tensor

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By Nan Sheng, Lexing Ying
| arxiv.org
[Submitted on 8 Aug 2024 ( v1 ), last revised 26 Jun 2025 (this version, v2)] Title:Solving high-dimensional Hamilton-Jacobi-Bellman equation with functional hierarchical tensor View a PDF of the paper titled Solving high-dimensional Hamilton-Jacobi-Bellman equation with functional hierarchical tensor, by Xun Tang and 1 other authors View PDF HTML (experimental) Abstract:This work proposes a novel numerical scheme for solving the high-dimensional Hamilton-Jacobi-Bellman equation with a...
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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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