The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Sequential testing with tailored stopping rules lets model evaluation halt early once statistical needs (CI width, significance, equivalence) are met, saving up to 80% compute on VLM leaderboards.
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A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
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Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Sequential testing with tailored stopping rules lets model evaluation halt early once statistical needs (CI width, significance, equivalence) are met, saving up to 80% compute on VLM leaderboards.