1 citations · 1 across the 5 of their papers we have counts for
5 papers
Universal priors: solving empirical Bayes via Bayesian inference and pretraining
Nick Cannella, Anzo Teh, Yanjun Han +1
We theoretically justify the recent empirical finding of [Teh et al., 2025] that a transformer pretrained on synthetically generated data achieves strong performance on empirical B…
Function estimation in the empirical Bayes setting
Benjamin Kang, Yury Polyanskiy, Anzo Teh
We study function estimation in the empirical Bayes setting for Poisson and normal means. Specifically, given observations with latent parameters $θ_i\sim π…
Solving Empirical Bayes via Transformers
Anzo Teh, Mark Jabbour, Yury Polyanskiy
This work applies modern AI tools (transformers) to solving one of the oldest statistical problems: Poisson means under empirical Bayes (Poisson-EB) setting. In Poisson-EB a high-d…
Empirical Bayes via ERM and Rademacher complexities: the Poisson model
Soham Jana, Yury Polyanskiy, Anzo Teh +1
We consider the problem of empirical Bayes estimation for (multivariate) Poisson means. Existing solutions that have been shown theoretically optimal for minimizing the regret (exc…
Comparing Poisson and Gaussian channels (extended)
Anzo Teh, Yury Polyanskiy
Consider a pair of input distributions which after passing through a Poisson channel become -close in total variation. We show that they must necessarily then be -…