Nonconvex projected gradient descent for noisy inductive matrix completion achieves linear convergence and order-optimal error at sample complexity scaling with side-information dimension a instead of ambient dimension n.
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Three-average primal-dual methods achieve accelerated rates for computable accuracy certificates in convex optimization.
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Sample efficient inductive matrix completion with noise and inexact side information
Nonconvex projected gradient descent for noisy inductive matrix completion achieves linear convergence and order-optimal error at sample complexity scaling with side-information dimension a instead of ambient dimension n.
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Accuracy Certificates for Convex Optimization at Accelerated Rates via Primal-Dual Averaging
Three-average primal-dual methods achieve accelerated rates for computable accuracy certificates in convex optimization.