Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Nobel, Parth"'
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to optimally tune hyperparameters. Cross-validation (CV) serves as the de f
Externí odkaz:
http://arxiv.org/abs/2409.09781
We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the number of these safe boxes is large, and basic preprocessing of them (such
Externí odkaz:
http://arxiv.org/abs/2305.01072
Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the $\ell_2$ risk of any estimator of the mean of a Gaussian random vector. We focus here on the case when the estimator minimizes a quadratic loss term plus a convex regularizer. Fo
Externí odkaz:
http://arxiv.org/abs/2211.05947
Publikováno v:
Optimization Letters 17, 1229-1240 (2023)
In recent work Simkin shows that bounds on an exponent occurring in the famous $n$-queens problem can be evaluated by solving convex optimization problems, allowing him to find bounds far tighter than previously known. In this note we use Simkin's fo
Externí odkaz:
http://arxiv.org/abs/2112.03336
Publikováno v:
IEEE Transactions on Robotics; 2024, Vol. 40 Issue: 1 p3795-3811, 17p
Publikováno v:
Optimization Letters; Jun2023, Vol. 17 Issue 5, p1229-1240, 12p
Publikováno v:
Natural Computing; Jun2021, Vol. 20 Issue 2, p287-306, 20p