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StatLoRA leverages statistical hypothesis testing to optimize rank allocation in LoRA, achieving superior performance while maintaining efficiency.
Projected quantum kernels can drastically improve sample efficiency in Gaussian process optimization, outperforming traditional high-dimensional quantum kernels.
Free exploration in multi-armed bandits can lead to sharp phase transitions in accumulated regret, offering significant savings compared to standard regret minimization.
