NEURIPS Poster "gradient descent" Papers

6 papers found

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Complexity Scaling Laws for Neural Models using Combinatorial Optimization

Lowell Weissman, Michael Krumdick, A. Abbott

NEURIPS 2025posterarXiv:2506.12932

Convergence Rates for Gradient Descent on the Edge of Stability for Overparametrised Least Squares

Lachlan MacDonald, Hancheng Min, Leandro Palma et al.

NEURIPS 2025posterarXiv:2510.17506

MAP Estimation with Denoisers: Convergence Rates and Guarantees

Scott Pesme, Giacomo Meanti, Michael Arbel et al.

NEURIPS 2025posterarXiv:2507.15397
2
citations

New Perspectives on the Polyak Stepsize: Surrogate Functions and Negative Results

Francesco Orabona, Ryan D'Orazio

NEURIPS 2025posterarXiv:2505.20219
5
citations

Simple and Optimal Sublinear Algorithms for Mean Estimation

Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn et al.

NEURIPS 2025posterarXiv:2406.05254

Transformers are almost optimal metalearners for linear classification

Roey Magen, Gal Vardi

NEURIPS 2025posterarXiv:2510.19797
1
citations