Two new research papers introduce novel methods for improving ensemble models in machine learning. The first, PACE, combines pruning and compression techniques to create more efficient and interpretable ensembles, outperforming existing methods. The second, Perturb-and-Correct (P&C), uses post-hoc perturbations on a single pretrained network to generate diverse predictors that maintain agreement on calibration data while differing elsewhere. P&C demonstrates a strong trade-off between in-distribution and out-of-distribution performance. AI
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IMPACT These papers explore techniques to enhance the efficiency and robustness of machine learning models, potentially leading to better performance in complex prediction tasks.
RANK_REASON Two academic papers published on arXiv present new methods for improving ensemble models.