Stochastic mirror ascent provably finds an approximately least-favorable distribution and minimax estimator for the Bounded Normal Mean problem, yielding 6–18% risk improvements over the minimax linear estimator.
(2026): Some existence results for maximin priors in statistical minimax theorems, Working paper, University of Southern California
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Approximate Minimax Estimation of a Bounded Normal Mean via Stochastic Mirror Ascent
Stochastic mirror ascent provably finds an approximately least-favorable distribution and minimax estimator for the Bounded Normal Mean problem, yielding 6–18% risk improvements over the minimax linear estimator.