We lifted 6 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sushrutk/robust_sparse_mean_estimation | canonical | 3 of 3 |
| huihui0902/robust_mean_estimation | pwc_unofficial | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| acc | Ran | huihui0902/robust_mean_estimation/robust_mean_estimate_acc.py code served (permissive licence) · get_code("cf656e059d09ed94") |
| err | Ran | sushrutk/robust_sparse_mean_estimation/robustlib.py code served (permissive licence) · get_code("88cf0fae09eb4f9c") |
| err | Ran | huihui0902/robust_mean_estimation/robustlib.py code served (permissive licence) · get_code("7f4024450c5b5604") |
| err_rspca | Ran | sushrutk/robust_sparse_mean_estimation/robustlib.py code served (permissive licence) · get_code("292414dc4e6ce863") |
| get_error | Ran | sushrutk/robust_sparse_mean_estimation/robustlib.py code served (permissive licence) · get_code("88e19c4ec091c85b") |
| pre_processing | Ran | huihui0902/robust_mean_estimation/robust_mean_estimate_acc.py code served (permissive licence) · get_code("3e161617d69a519f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems. In more detail, our algorithms are sample and computationally efficient and achieve near-optimal robustness guarantees. In contrast to prior provable algorithms which relied on the ellipsoid method, our algorithms use spectral techniques to iteratively remove outliers from the dataset. Our experimental evaluation on synthetic data shows that our algorithms are scalable and significantly outperform a range of previous approaches, nearly matching the best error rate without corruptions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1911.08085")
get_code_for_paper("1911.08085")
have("1911.08085")
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