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Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks
The research community often relies on a model's average performance across the test prompts of a benchmark to evaluate the model's performance.
Causal Discovery for Manufacturing Domains
Specifically, the goal of this work is to learn interpretable causal models from observational data produced by manufacturing lines.
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks.
Reasoning about Independence in Probabilistic Models of Relational Data
We extend the theory of d-separation to cases in which data instances are not independent and identically distributed.
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