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The University of Mel- bourne
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TimeLAVA reveals that a learning-agnostic approach can significantly enhance data valuation in time series, outperforming traditional methods by effectively capturing temporal dynamics.
Aggregating insights from diverse causal discovery experts with LLM-guided reweighting leads to significantly improved causal graph accuracy, even in ambiguous scenarios.
