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sparho 0.2.0
sparho 0.2.0

User guide

  • Installation
  • Quickstart
  • Concepts
  • Protocols & extension points
  • API stability
  • Reproducibility
  • Migrating from sparse-ho

Theory

  • Theory
    • Implicit differentiation
    • Active sets and why we restrict
    • Penalties: prox, Jacobian, \(\partial_\beta s\), \(\partial_\alpha s\)
    • Criteria and the outer chain rule
    • Convergence: HOAG outer loop
    • References
  • Implicit differentiation
  • Active sets and why we restrict
  • Penalties: prox, Jacobian, \(\partial_\beta s\), \(\partial_\alpha s\)
  • Criteria and the outer chain rule
  • Convergence: HOAG outer loop
  • References

How-to

  • Standardization and CV leakage

API reference

  • API reference
    • Problem definition
    • Solvers
    • Criteria
    • Hypergradient
    • Outer-search loops
    • Result and state dataclasses
    • Type aliases
    • sklearn-compatible wrappers

Examples

  • Examples gallery
    • Migrating from sparse-ho to sparho
    • Sparse logistic regression
    • Weighted Lasso (per-feature α)
    • Held-out Lasso with HOAG
    • SURE-tuned Lasso (no held-out set)
    • Cross-validated Lasso
    • Group-Lasso with HOAG
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