In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred.

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Instead,ourboundrequires asimple, intuitive condition which is well justified by prior empirical works and holds in practiceeffectively100%ofthetime. Theboundisinspiredby H H-divergence but is easier to evaluate and substantially tighter, consistently providing nonvacuous test error upper bounds.

Differentsamplingapproaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees.

Recall therealoptimal n (optimal P) and n isdenoted n( ). Withac0 and solve Eq. 10 exactlyby29].

Finally, generate a new brief description mainly based on the originalnull description with attributes information incorporated. Thenull narratives should be similar to the original description.null

Specifically, we resort to large language models (LLM) and large multimodal models (LMM) with our proposed Statics and Dynamics Enhanced Captioning modules to generate diverse fine-grained captions for each video.