arXiv Machine Learning June 19, 2026 By Dongmin Lee, William Lu, Anuran Makur

On the Oracle Complexity of Interpolation-Based Gradient Descent

Read the original on arXiv Machine Learning →

arXiv:2606. 19878v1 Announce Type: new Abstract: Recent work on first-order optimizers for empirical risk minimization (ERM) has suggested that smoothness of ERM loss functions in the training data, rather than in the optimization parameters, can be leveraged to improve the oracle complexity of gradient descent (GD) methods.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 2

Fast Generalization after Interpolation via Critically Damped Momentum Optimization

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

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arXiv Machine Learning

Jun 30

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

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 Machine Learning

Jul 27

Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs

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 Machine Learning

Jun 8

Second-Order Path Kernel Interpolation Formulas in Machine Learning

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 Machine Learning

Jul 13

Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization

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By Chris Junchi Li

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arXiv Machine Learning

Jul 21

Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach

arXiv:2601. 21243v3 Announce Type: replace-cross Abstract: We consider max-min and min-max problems with objective functions that are possibly non-smooth, submodular with respect to the minimiser and concave with respect to the maximiser.

By Amir Ali Farzin, Yuen-Man Pun, Philipp Braun, Tyler Summers, Iman Shames

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Fast Generalization after Interpolation via Critically Damped Momentum Optimization

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