Adaptive Gradient Methods at the Edge of Stability. - Jeremy Cohen 001; Behrooz Ghorbani; Shankar Krishnan; Naman Agarwal; Sourabh Medapati; Michal Badura; Daniel Suo; David Cardoze; Zachary Nado; George E. Dahl; Justin Gilmer (CoRR, 2022)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

Amortized Planning with Large-Scale Transformers: A Case Study on Chess. - Anian Ruoss; Grégoire Delétang; Sourabh Medapati; Jordi Grau-Moya; Kevin Li; Elliot Catt; John Reid; Cannada Lewis; Joel Veness; Tim Genewein (NeurIPS, 2024)

Grandmaster-Level Chess Without Search. - Anian Ruoss; Grégoire Delétang; Sourabh Medapati; Jordi Grau-Moya; Li Kevin Wenliang; Elliot Catt; John Reid; Tim Genewein (CoRR, 2024)

Towards flexible perception with visual memory. - Robert Geirhos; Priyank Jaini; Austin Stone; Sourabh Medapati; Xi Yi; George Toderici; Abhijit Ogale; Jonathon Shlens (CoRR, 2024)

Accelerating neural network training: An analysis of the AlgoPerf competition. - Priya Kasimbeg; Frank Schneider 001; Runa Eschenhagen; Juhan Bae; Chandramouli Shama Sastry; Mark Saroufim; Boyuan Feng; Less Wright; Edward Z. Yang; Zachary Nado; Sourabh Medapati; Philipp Hennig; et al. (ICLR, 2025)

How far away are truly hyperparameter-free learning algorithms? - Priya Kasimbeg; Vincent Roulet; Naman Agarwal; Sourabh Medapati; Fabian Pedregosa; Atish Agarwala; George E. Dahl (Trans. Mach. Learn. Res., 2025)

Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW. - Sourabh Medapati; Priya Kasimbeg; Shankar Krishnan; Naman Agarwal; George E. Dahl (CoRR, 2025)

Learning by solving differential equations. - Benoit Dherin; Michael Munn; Hanna Mazzawi; Michael Wunder; Sourabh Medapati; Javier Gonzalvo (CoRR, 2025)