Transformers trained to imitate Bayesian posterior Neyman allocations achieve smoothness-adaptive ATE estimation via mixture-of-experts in-context learning.
Martingale limit theory and its application
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Constant stepsize SA with decision-dependent Markovian noise has stationary bias O(alpha) under Poisson-Gateaux differentiability, plus finite-time moment bounds and weak convergence.
citing papers explorer
-
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Transformers trained to imitate Bayesian posterior Neyman allocations achieve smoothness-adaptive ATE estimation via mixture-of-experts in-context learning.
-
Revisiting the Constant Stepsize Stochastic Approximation with Decision-Dependent Markovian Noise
Constant stepsize SA with decision-dependent Markovian noise has stationary bias O(alpha) under Poisson-Gateaux differentiability, plus finite-time moment bounds and weak convergence.