Develops a CLT framework for locally dependent scores on marked Euclidean point processes via geometric mixing and bounded-Lipschitz localization, with applications to spin systems and interacting particles.
Limit theory for geometric statistics of point processes having fast decay of correlations
2 Pith papers cite this work. Polarity classification is still indexing.
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Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.
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Limit theory for Lipschitz-localized statistics in random geometric models
Develops a CLT framework for locally dependent scores on marked Euclidean point processes via geometric mixing and bounded-Lipschitz localization, with applications to spin systems and interacting particles.
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Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.