Selected Works

• G.G., J. Hu, L. Lin, A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms, International Conference on Machine Learning (ICML 2026).
• G.G., J. Hu, L. Lin, Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing, SIAM Journal on Matrix Analysis and Applications (SIMAX 2026, to appear).
• A. Guzmán-Cordero, F. Dangel, G.G., M. Zeinhofer, Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization, Neural Information Processing Systems (NeurIPS 2025).
• E. Epperly, G.G., R. Webber, Randomized Kaczmarz with Tail Averaging, Applied and Computational Harmonic Analysis (ACHA 2025).
• G.G., N. Abrahamsen, L. Lin, A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions, Journal of Computational Physics (JCP 2024).
  
  

See Google Scholar for More