Estimation.OrthogonalLearning.Sparse
Sparse finite-dimensional plug-in ERM theory: restricted strong convexity, restricted cones, one-norm geometry, and lasso-style oracle guarantees.
RSC 1 core · 0 supporting This file defines restricted strong convexity for a finite-dimensional empirical risk at the population target over the restricted cone associated with a support set.
Restricted Strong Convexity
This file defines restricted strong convexity for a finite-dimensional empirical risk at the population target over the restricted cone associated with a support set. The condition supplies the curvature input in the sparse plug-in estimation guarantee.
The exported predicate RestrictedStrongConvexity states that the empirical
risk lies above its first-order approximation at θ₀ by a quadratic margin on
RestrictedCone S₀; positivity of the curvature parameter is supplied by
callers.
For a finite coordinate dimension, an empirical-risk function, a specified gradient-like vector-valued function, a reference coefficient vector, a support set, and a real curvature constant, restricted strong convexity means that, for every vector in the restricted cone of the support, the empirical-risk increase minus the inner product of that perturbation with the specified vector-valued function at the reference vector is at least one half the curvature constant times the perturbation's squared Euclidean norm.
Definition (Lean source)
Setup 7 core · 1 supporting This file develops the finite-dimensional sparse target geometry used by the orthogonal statistical-learning plug-in analysis, including restricted and full coordinate one-norms, the restricted cone around a support set, ★ mem_RestrictedCone_iff★ l1Norm_supp_le_card_sqrt_mul_l2norm
Sparse Plug-In Setup
This file develops the finite-dimensional sparse target geometry used by the orthogonal statistical-learning plug-in analysis, including restricted and full coordinate one-norms, the restricted cone around a support set, and exact penalized empirical risk minimizers. It specializes the abstract target space to Euclidean coordinates suitable for sparsity arguments.
The geometry is exposed through l1Norm, l1Full, linftyDev, and
RestrictedCone, with l1Full_eq and l1Norm_supp_le_card_sqrt_mul_l2norm
supplying the norm identities and bounds used in the sparse guarantee. The
estimator predicate is SparsePluginERM.
For a finite coordinate dimension, a coefficient vector, and a set of coordinates, the restricted coordinate one-norm is the sum of the absolute values of that vector's coordinates in the specified set.
Definition (Lean source)
For a finite coordinate dimension and a coefficient vector, the full coordinate one-norm is the sum of the absolute values of all its coordinates.
Definition (Lean source)
For a finite coordinate dimension, a proof that its coordinate set is nonempty, and a coefficient vector, the coordinate infinity-norm deviation is the largest absolute coordinate value of that vector.
For a finite coordinate dimension and a designated support set of coordinates, the restricted cone consists of exactly those coefficient vectors whose coordinate one-norm outside the support is no greater than three times their coordinate one-norm on the support.
Restricted cone membership unfolded. A vector ν lies in the restricted cone RestrictedCone S₀ exactly when its ℓ¹ norm off the support S₀ is at most three times its ℓ¹ norm on S₀.
Formal statement
Proof (Lean source)
Cauchy–Schwarz on the support. The ℓ¹ norm of a vector ν restricted to a finite index set S₀ is bounded by √|S₀| times its full ℓ² norm.
Formal statement
Proof (Lean source)
Sparse plug-in regularised ERM (predicate form). Given an empirical risk with the plug-in nuisance already absorbed and a candidate parameter estimate, this predicate records that the ℓ₁-penalty level is nonnegative and that the candidate minimises the ℓ₁-penalised empirical risk over the entire ambient parameter space, with no restriction to a support set.
Definition (Lean source)
θhat minimises the penalised objective over the ambient space.
1 supporting declaration (lemmas, instances)
-
l1Full_eqlemma — l1Full decomposes along any subset and its complement in Finset.univ.hypotheses
PluginGuarantee 3 core · 0 supporting This file proves the deterministic sparse plug-in guarantee for penalized empirical risk minimization under restricted strong convexity, support sparsity, a first-order condition at the target, and a gradient-deviation b ★ sparse_plugin_guarantee★ sparse_plugin_guarantee_highProb
Sparse Plug-In Guarantee
This file proves the deterministic sparse plug-in guarantee for penalized empirical risk minimization under restricted strong convexity, support sparsity, a first-order condition at the target, and a gradient-deviation bound. It also states the high-probability interface that will turn a tail bound for the gradient deviation into the same sparse estimation guarantee.
The deterministic theorem sparse_plugin_guarantee proves membership of
θhat - θ₀ in RestrictedCone S₀ and the displayed 12 * lambda * sqrt s / σn
error bound. The predicate LinftyDevTailBound and theorem
sparse_plugin_guarantee_highProb lift this deterministic guarantee to a
high-probability event.
Deterministic sparse plug-in ERM guarantee. Let empRiskFn be a convex empirical risk function on EuclideanSpace ℝ (Fin p), with population gradient popGrad and empirical gradient map gradEmp. Assume the support S₀ of the truth θ₀ has cardinality s, that θ₀ vanishes off S₀, that the restricted-strong-convexity modulus σn is strictly positive and empRiskFn is σn-restricted-strongly-convex at θ₀ over S₀, and that the empirical gradient at θ₀ satisfies the subgradient basic inequality for empRiskFn. Given a penalty level with lambda > 0, suppose the index set Fin p is nonempty so that lambda is at least twice the sup-norm deviation of the empirical gradient from the population gradient at θ₀, that the population gradient vanishes at the truth (the first-order condition), and that θhat is a sparse plug-in regularised empirical-risk minimizer at penalty level lambda. Then the estimation error θhat − θ₀ lies in the restricted cone around S₀, and its Euclidean norm is at most 12 · lambda · √s / σn.
Formal statement
Proof (Lean source)
Given a measure on a sample space, a nonempty finite coordinate set, a sample-indexed vector-valued deviation field, a deviation threshold, and a confidence tolerance, the high-probability sup-norm tail-bound condition holds exactly when there exists an event that is measurable, has measure at least , with subtraction truncated at zero, and on which the maximum absolute coordinate of the deviation field is at most the threshold.
Definition (Lean source)
High-probability sparse plug-in ERM guarantee. Let empRiskFn(ω) be a random empirical risk function on EuclideanSpace ℝ (Fin p) with random empirical gradient map gradEmp(ω), and popGrad the population gradient. Assume the support S₀ of the truth θ₀ has cardinality s, that θ₀ vanishes off S₀, and that empRiskFn(ω) is convex on the ambient space for every ω. Suppose the restricted-strong- convexity modulus σn is strictly positive and empRiskFn(ω) is σn-restricted-strongly-convex at θ₀ over S₀ for every ω, and that the empirical gradient at θ₀ satisfies the subgradient basic inequality for empRiskFn(ω), for every ω. Given a penalty level with lambda > 0, and assuming the index set Fin p is nonempty, that the population gradient vanishes at the truth, and that θhat(ω) is a sparse plug-in regularised empirical-risk minimizer of empRiskFn(ω) at penalty level lambda, for every ω. Suppose further that the deviation of the empirical gradient from popGrad at θ₀ obeys a sup-norm tail bound ρ at confidence level δ, with lambda at least twice that tail level, lambda ≥ 2ρ. Then there is an event of probability at least 1 - δ on which, for every ω in it, the estimation error θhat ω − θ₀ lies in the restricted cone around S₀ and its Euclidean norm is at most 12 · lambda · √s / σn.