Research map / Learning theory and algorithms / Matrix and tensor methods
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Doubly greedy primal-dual coordinate updates with active sets (DGPD) vs. baselines | L1+L2-regularized ERM on multi-class datasets · Mnist-RF, Aloi-RF, RCV1, Mnist-RB, Aloi-RB, Sector | running time to reach relative primal objective: up to 30x speedup claimed | Helped |
Closest papers by meaning
- Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity (NeurIPS 2018 · similarity 0.91)
- Random extrapolation for primal-dual coordinate descent (ICML 2020 · similarity 0.90)
- Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms (ICML 2017 · similarity 0.90)
- Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization (NeurIPS 2017 · similarity 0.88)
- Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression (NeurIPS 2023 · similarity 0.87)
- On the Efficiency of ERM in Feature Learning (NeurIPS 2024 · similarity 0.86)
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