Sparse Signal Recovery under Coherence, Outliers, and Group Structure¶
- Task ID:
math.spike_slab_em_sparse_signal_recovery - Domain:
math - Subdomain:
bayesian_inference - Status:
test - Tags:
sparse_regression,spike_and_slab,em_algorithm,robust_regression,huber_loss,non_gaussian_noise,group_lasso,proximal_gradient,high_coherence_dictionary,cross_validation,support_recovery
Public Summary¶
This page is generated from task metadata and selected public-safe excerpts.
Example B1 Prompt Excerpt¶
Implement a Python program for sparse recovery under a difficult observation model.
Read only:
- `./data/design_matrix.npy`
- `./data/observations.npy`
- `./data/solver_config.json`
- `./data/group_structure.json`
Hints (medium level):
1. Data may contain outliers, so robust fitting (for example Huber) is safer than pure L2.
Notes¶
- This page is a generated site artifact.
- Higher-level prompt details and internal benchmark specifics may remain intentionally undisclosed.