On the Computational Tractability of Robust Bandits
Researchers give a polynomial-time robust bandit learner with near-square-root regret and show nearby variants are NP-hard.
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Researchers give a polynomial-time robust bandit learner with near-square-root regret and show nearby variants are NP-hard.
A deterministic polynomial-time algorithm achieves optimal O(sqrt d) regret for online inverse linear optimization.
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