4 papers
Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods
Xiliang Yang, Yifei Xiong, Zhijian He
There is a growing interest in studying sequential neural posterior estimation (SNPE) techniques due to their advantages for simulation-based models with intractable likelihoods. T…
DPO-Shift: Shifting the Distribution of Direct Preference Optimization
Xiliang Yang, Feng Jiang, Qianen Zhang +2
Direct Preference Optimization (DPO) and its variants have become increasingly popular for aligning language models with human preferences. These methods aim to teach models to bet…
A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization
Xiliang Yang, Shenyang Deng, Shicong Liu +3
Data augmentation is an important technique in training deep neural networks as it enhances their ability to generalize and remain robust. While data augmentation is commonly used…
An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation
Yifei Xiong, Xiliang Yang, Sanguo Zhang +1
Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods. Unlike approximate Bayes…