Fast Exact Conformalization of Lasso using Piecewise Linear Homotopy
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Conformal prediction is a general method that converts almost any point predictor to a prediction set. The resulting set keeps good statistical properties of the original estimator under standard assumptions, and guarantees valid average coverage even when the model is misspecified. A main challenge in applying conformal prediction in modern applications is efficient computation, as it generally requires an exhaustive search over the entire output space. In this paper we develop an exact and computationally efficient conformalization of the Lasso and elastic net. The method makes use of a novel piecewise linear homotopy of the Lasso solution under perturbation of a single input sample point. As a by-product, we provide a simpler and better justified online Lasso algorithm, which may be of independent interest. Our derivation also reveals an interesting accuracy-stability trade-off in conformal inference, which is analogous to the bias-variance trade-off in traditional parameter estimation. The practical performance of the new algorithm is demonstrated using both synthetic and real data examples.
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