The following pages link to L0Learn (Q52543):
Displaying 22 items.
- (Q71783) (redirect page) (← links)
- An extended Newton-type algorithm for \(\ell_2\)-regularized sparse logistic regression and its efficiency for classifying large-scale datasets (Q2033090) (← links)
- Sparse classification: a scalable discrete optimization perspective (Q2071494) (← links)
- Robust subset selection (Q2076115) (← links)
- Mining events with declassified diplomatic documents (Q2078743) (← links)
- The backbone method for ultra-high dimensional sparse machine learning (Q2163249) (← links)
- Matrix completion with nonconvex regularization: spectral operators and scalable algorithms (Q2195855) (← links)
- Semi-automated simultaneous predictor selection for regression-SARIMA models (Q2209736) (← links)
- Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons (Q2225312) (← links)
- A discussion on practical considerations with sparse regression methodologies (Q2225315) (← links)
- Discussion of ``Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons'' (Q2225316) (← links)
- Rejoinder: ``Sparse regression: scalable algorithms and empirical performance'' (Q2225319) (← links)
- Rejoinder: ``Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons'' (Q2225320) (← links)
- Graph structured sparse subset selection (Q2662712) (← links)
- Randomized Gradient Boosting Machine (Q4971024) (← links)
- A Mixed-Integer Fractional Optimization Approach to Best Subset Selection (Q4995087) (← links)
- (Q4998944) (← links)
- MIP-BOOST: Efficient and Effective <i>L</i><sub>0</sub> Feature Selection for Linear Regression (Q5066443) (← links)
- Subset selection in network-linked data (Q5086104) (← links)
- Scalable Algorithms for the Sparse Ridge Regression (Q5148400) (← links)
- (Q5159402) (← links)
- The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty (Q5162621) (← links)
