arXiv Machine Learning By Omar Al-Ghattas

Risk reversal for least squares estimators under nested convex constraints

Read the original on arXiv Machine Learning →

arXiv:2601. 16041v2 Announce Type: replace-cross Abstract: In constrained stochastic optimization, one expects that restricting the feasible set, provided it still contains the true parameter, should not increase the statistical risk of the corresponding projection estimator.

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arXiv Machine Learning
Jun 10

Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization

arXiv:2509. 17251v2 Announce Type: replace-cross Abstract: Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regression and online stochastic gradient descent (SGD) are polynomially suboptimal for certain categories of such problems.

By Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu