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  "Title": "Adversarial Random Forests for Omics Synthesis",
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  "Description": "We extend Adversarial Random Forests to a high-dimensional\nframework. The method partitions the feature space into regions\nwhere the assumption of feature independence within tree leaves\nis more likely to hold. Region-specific adversarial random\nforest models are trained to capture local dependence\nstructures, while an additional adversarial random forest is\nfitted to a meta-space representation to model dependencies\nbetween regions. New observations are generated by first\nsampling from the meta-space model and then conditionally\nsampling from each region-specific model. The proposed\nmethodology is described in Fouodo et al. (2026)\n<doi:10.64898/2026.09.09.750490>.",
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