Senior Principal Researcher, Microsoft Research New England
2 papers at NeurIPS 2025
We show that the statistical optimality of estimation methods for causal inference depend in a surprising way on the distribution of the treatment noise.
We propose an informed corrector for masked discrete diffusion that reduces approximation errors, enabling faster sampling and better sample quality in both synthetic and large-scale settings.
