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Learning Sparse Distributions using Iterative Hard Thresholding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IHT after Greedy initialization instead of Greedy selection alone | Sparse distribution learning · Simulated data, normalized ℓ2-norm minimization | normalized ℓ2 objective | Helped |
| Use IHT after Greedy initialization instead of Greedy selection alone | Sparse distribution learning · Simulated data, normalized KL minimization | normalized KL objective | Helped |
| Use IHT after Greedy initialization instead of Greedy selection alone | Sparse distribution learning · Simulated data | run-to-run stability | Helped |
| Use IHT with greedy projections instead of post-processed Lasso or Random | Distribution compression · Texas hospital discharge dataset | moment reconstruction squared error | Helped |
| Use IHT instead of Local Greedy, Global Greedy, or PS | Representative prototype selection · Digits | test nearest-neighbor classification error | Helped |
| Use IHT instead of two-stage gradient descent followed by greedy projection | Sparse distribution learning · ℓ2-norm minimization | objective | Helped |
Closest papers by meaning
- Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations (NeurIPS 2024 · similarity 0.88)
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- Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence (ICML 2024 · similarity 0.85)
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