Proposes an adaptive hybrid estimator for common mean estimation under independent but non-identical symmetric unimodal distributions, with near-optimality guarantees even when only log n / n samples are low-noise.
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The specular gradient method, a variant of the subgradient method, converges root-linearly for 1D convex functions under convexity alone.
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Estimating location parameters in entangled single-sample distributions
Proposes an adaptive hybrid estimator for common mean estimation under independent but non-identical symmetric unimodal distributions, with near-optimality guarantees even when only log n / n samples are low-noise.
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Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence
The specular gradient method, a variant of the subgradient method, converges root-linearly for 1D convex functions under convexity alone.