Sparse Learning

43 papers with code • 3 benchmarks • 3 datasets

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Libraries

Use these libraries to find Sparse Learning models and implementations

Most implemented papers

SparseStep: Approximating the Counting Norm for Sparse Regularization

GjjvdBurg/SparseStep 24 Jan 2017

The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem.

From safe screening rules to working sets for faster Lasso-type solvers

mathurinm/A5G 21 Mar 2017

For the Lasso estimator a WS is a set of features, while for a Group Lasso it refers to a set of groups.

Learning task structure via sparsity grouped multitask learning

meghana-kshirsagar/treemtl 13 May 2017

We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.

Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low

antoine-dedieu/subset_selection_with_shrinkage 10 Aug 2017

We conduct an extensive theoretical analysis of the predictive properties of the proposed approach and provide justification for its superior predictive performance relative to best subset selection when the noise-level is high.

Sparse learning of stochastic dynamic equations

dynamicslab/langevin-regression 6 Dec 2017

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets.

Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

hazimehh/L0Learn 5 Mar 2018

In spite of the usefulness of $L_0$-based estimators and generic MIO solvers, there is a steep computational price to pay when compared to popular sparse learning algorithms (e. g., based on $L_1$ regularization).

Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking

lolimay1999/Homepage ECCV 2018

To address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking.

Optimal approximation for unconstrained non-submodular minimization

marwash25/non-sub-min ICML 2020

Submodular function minimization is well studied, and existing algorithms solve it exactly or up to arbitrary accuracy.

RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering

roccojhu/neural_regression_discontinuity 10 Jun 2019

Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

Raymw/Federated-XGBoost 16 Jul 2019

Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance.