BO algorithms expand unknown search spaces via hyperharmonic series control to achieve sub-linear cumulative regret bounds, with a high-dimensional variant.
Scikit-learn: Machine learning in python
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Activation verbalization methods for LLMs largely reflect the verbalizer model's parametric knowledge rather than privileged information from the target model's activations.
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Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
BO algorithms expand unknown search spaces via hyperharmonic series control to achieve sub-linear cumulative regret bounds, with a high-dimensional variant.
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Do Activation Verbalization Methods Convey Privileged Information?
Activation verbalization methods for LLMs largely reflect the verbalizer model's parametric knowledge rather than privileged information from the target model's activations.