Probability
Concentration inequalities, random matrices, stochastic processes, high-dimensional probability.
2 items · All topics
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Diffusion Removes Langevin's Conditioning Dependence: A Sharp Gaussian Analysis
For Gaussian targets, tuned diffusion sampling avoids the condition-number penalty that Langevin methods pay; both rates are sharp.
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A stochastic subgradient method with optimal failure exponent
Averaged subgradient descent attains the sharp large-deviation exponent under sub-Gaussian noise, and a matching lower bound shows the constant is optimal.