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cs.LG2026
Towards Disentangled Preference Optimization Dynamics: Suppress the Loser, Preserve the Winner
Wei Chen, Yubing Wu, Junmei Yang +5
Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based methods also suppress the chosen response when they…
cs.LG2025
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Jian Xu, Zhiqi Lin, Min Chen +3
Deep Gaussian process models typically employ discrete hierarchies, but recent advancements in differential Gaussian processes (DiffGPs) have extended these models to infinite dept…
cs.LG2024
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling
Jian Xu, Zhiqi Lin, Shigui Li +4
Bayesian Last Layer (BLL) models focus solely on uncertainty in the output layer of neural networks, demonstrating comparable performance to more complex Bayesian models. However,…