<ul><li>Machine learning algorithms in high-dimensional settings are highly susceptible to the influence of even a small fraction of structured outliers, making robust optimization techniques essential.</li><li>New novel algorithms have been developed that achieve minimax-optimal excess risk (up to logarithmic factors) under the epsilon-contamination model.</li><li>These algorithms do not require stringent assumptions, such as Lipschitz continuity and smoothness of individual sample functions, improving over existing suboptimal algorithms.</li><li>The developed algorithms can also handle the case of unknown covariance parameter and can be extended to nonsmooth population risks via convolutional smoothing.</li></ul>

Optimal Rates for Robust Stochastic Convex Optimization

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