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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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3 results for “ElasticNet”

Provably tuning the ElasticNet across instancescs.LG

We solve ElasticNet regularization tuning across multiple instances with provable guarantees.

problem Tuning ElasticNet regularization coefficients across multiple problem instances.

method Characterized ElasticNet loss as a piecewise-rational function, derived structural complexity bounds, and showed generalization and online learning guarantees.

result First general learning-theoretic guarantees for ElasticNet tuning without strong data distribution assumptions.

ElasticNet Regularization Tuning Piecewise-rational function Generalization

2022-07-20abs ↗pdf ↗

Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworkscs.LG

Study evaluates scikit-learn regularization frameworks for machine learning models.

problem Choosing the right regularization framework for applied machine learning models.

method Empirical evaluation of four canonical frameworks (Ridge, Lasso, ElasticNet, Post-Lasso OLS) across 134,400 simulations.

result Lasso recall is highly fragile under multicollinearity; at high condition numbers (kappa) and low SNR, Lasso recall collapses to 0.18 while ElasticNet maintains 0.93.

regularization scikit-learn machine learning simulation Lasso

2026-04-04abs ↗pdf ↗

G-computation for increasing performances of clinical trials with individual randomization and binary responsestat.ME

G-computation improves clinical trial power with machine learning.

problem Balancing prognostic factors in randomized trials to prevent near-confounders.

method G-computation with penalized models (Lasso, Elasticnet) and algorithm-based methods (neural network, SVM, super learner).

result G-computation with Elasticnet and splines reduces variance and increases power in RCTs.

clinical trials randomized controlled trials G-computation machine learning near-confounders

2024-11-15abs ↗pdf ↗