Best-action queries yield Õ(min{T/k, √(T-k)}) regret for i.i.d. stochastic rewards but only Ω(√(T-k)) regret for correlated stochastic or adversarial rewards in the bandit-feedback model.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A new TWCTV regularizer using weighted Schatten-p norms on gradients and adaptive sparse weighting in the M-product framework is proposed for robust tensor completion, with an ADMM solver and claimed superior performance on image tasks.
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Multi-Armed Bandits With Best-Action Queries
Best-action queries yield Õ(min{T/k, √(T-k)}) regret for i.i.d. stochastic rewards but only Ω(√(T-k)) regret for correlated stochastic or adversarial rewards in the bandit-feedback model.
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Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularization
A new TWCTV regularizer using weighted Schatten-p norms on gradients and adaptive sparse weighting in the M-product framework is proposed for robust tensor completion, with an ADMM solver and claimed superior performance on image tasks.