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We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries.
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Optimal algorithms for online convex optimization with multi-point bandit feedback
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Bandit convex optimization: Towards tight bounds
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Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
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On asymptotically optimal confidence regions and tests for high-dimensional models
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Confidence intervals for low dimensional parameters in high dimensional linear models
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Kernel-based methods for bandit convex optimization
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