
CDS Asst. Prof. Yanjun Han (@yanjun_han) and colleagues at NYU and MIT explains why transformers trained on synthetic data excel at empirical Bayes (EB) problems. By using universal priors, these models adapt to new data through posterior contraction. arxiv.org/abs/2602.15136












