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Worst-case adversarial bandits no longer have to learn from scratch: transferring predictable task-level priors across random action sets achieves provably sublinear transfer regret with intrinsic-dimension $\mathcal{O}(\sqrt{n})$ guarantees.
A unified benchmark reveals the trade-offs between pixel-wise accuracy and perceptual realism in state-of-the-art image super-resolution techniques.
