# Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems

arXiv: https://arxiv.org/abs/1810.02716 · HTML: https://mlsanity.com/paper/1810.02716/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [Mitigating multiple descents: A model-agnostic framework for risk monotonization](https://mlsanity.com/paper/2205.12937/) (2022)
- [A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond](https://mlsanity.com/paper/2205.10198/) (2022)
- [ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data](https://mlsanity.com/paper/2406.11666/) (2024)
- [Approximate Leave-one-out Cross Validation for Regression with ℓ1 Regularizers (extended version)](https://mlsanity.com/paper/2310.17629/) (2023)
- [Simultaneous analysis of approximate leave-one-out cross-validation and mean-field inference](https://mlsanity.com/paper/2501.02624/) (2025)
- [Accelerating Conformal Prediction via Approximate Leave-One-Out](https://mlsanity.com/paper/2606.31915/) (2026)

## Abstract (arXiv)

Consider the following class of learning schemes: beginequation labeleq:main-problem1 hatboldsymbolβ := undersetboldsymbolβ ∈ Cargmin ;sumj=1n ℓ(boldsymbolxj^topboldsymbolβ; yj) + λ R(boldsymbolβ), qquad qquad qquad (1) endequation where boldsymbolxi ∈ ℝp and yi ∈ ℝ denote the irm th feature and response variable respectively. Let ℓ and R be the convex loss function and regularizer, boldsymbolβ denote the unknown weights, and λ be a regularization parameter. C subset ℝp is a closed convex set. Finding the optimal choice of λ is a challenging problem in high-dimensional regimes where both n and p are large. We propose three frameworks to obtain a computationally efficient approximation of the leave-one-out cross validation (LOOCV) risk for nonsmooth losses and regularizers. Our three frameworks are based on the primal, dual, and proximal formulations of (1). Each framework shows its strength in certain types of problems. We prove the equivalence of the three approaches under smoothness conditions. This equivalence enables us to justify the accuracy of the three methods under such conditions. We use our approaches to obtain a risk estimate for several standard problems, including generalized LASSO, nuclear norm regularization, and support vector machines. We empirically demonstrate the effectiveness of our results for non-differentiable cases.