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This lecture explores a novel approach to stabilizing model selection in data analysis. Learn how to address the instability problem in model selection, where removing a single data point can lead to completely different models being chosen. Rebecca Willett presents a new methodology combining bagging with an "inflated" argmax operation that selects multiple well-fitting models with theoretical stability guarantees. The approach ensures that even when training data points are removed, the resulting model collections will likely overlap with the original selection. The presentation demonstrates practical applications in ecosystem competition modeling and graph estimation using proteomics cell-signaling data, showing how this method produces stable, compact, and accurate model collections that outperform existing benchmarks. Understand joint research work conducted with Melissa Adrian and Jake Soloff that offers promising solutions to fundamental challenges in predictive modeling and dynamic biological process analysis.
