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DiscoPER is an autonomous framework leveraging large language models and dynamic code generation for open-ended scientific research, using second-order meta-reflection to synthesize discoveries and statistical testing for rigor. Evaluated on a multimodal ecological benchmark, it outperforms baselines in recovering known patterns.
This doctoral thesis critiques current fairness metrics in machine learning and proposes statistical hypothesis testing and structural analysis to address bias, emphasizing network and hierarchical contexts.