arXiv:2606. 01521v1 Announce Type: new Abstract: A central problem in machine learning is that models can achieve near-perfect training performance while generalizing substantially less well to unseen examples.
By Luca Muscarnera, Silas Ruhrberg Est\'evez, Yuanzhang Xiao, Mihaela Van der Schaar
The paper investigates overparameterized polynomial interpolation across three polynomial bases—Monomial, Chebyshev, and Legendre—using coefficients minimal in the σ^2-norm (and σ^1-norm for the monomial basis). It focuses on equidistant and Chebyshev data points, though many findings hold regardless of sampling specifics. The study draws parallels between the classical Runge phenomenon and the modern double descent phenomenon in machine learning.
By Jason Wein, Stephan Wojtowytsch
arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.
By Andrea Montanari, Kangjie Zhou
arXiv:2607. 22263v1 Announce Type: cross Abstract: A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including integer linear programming (ILP).
By Akira Kitaoka
arXiv:2606. 07495v1 Announce Type: new Abstract: Understanding how training data shape neural network predictions is a central problem in modern learning theory.
By Jin Guo, Roy Y. He, Jean-Michel Morel
arXiv:2405. 00914v4 Announce Type: replace-cross Abstract: We present in this paper novel accelerated fully first-order methods in \emph{Bilevel Optimization} (BLO).
By Chris Junchi Li