Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation. - Sanae Lotfi; Tiphaine Bonniot de Ruisselet; Dominique Orban; Andrea Lodi 001 (CoRR, 2020)

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. - Gregory W. Benton; Wesley J. Maddox; Sanae Lotfi; Andrew Gordon Wilson (ICML, 2021)

Dangers of Bayesian Model Averaging under Covariate Shift. - Pavel Izmailov; Patrick Nicholson; Sanae Lotfi; Andrew Gordon Wilson (NeurIPS, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning. - Sanae Lotfi; Tiphaine Bonniot de Ruisselet; Dominique Orban; Andrea Lodi 001 (CoRR, 2021)

Bayesian Model Selection, the Marginal Likelihood, and Generalization. - Sanae Lotfi; Pavel Izmailov; Gregory W. Benton; Micah Goldblum; Andrew Gordon Wilson (ICML, 2022)

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. - Sanae Lotfi; Marc Finzi; Sanyam Kapoor; Andres Potapczynski; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2022)

Non-Vacuous Generalization Bounds for Large Language Models. - Sanae Lotfi; Marc Anton Finzi; Yilun Kuang; Tim G. J. Rudner; Micah Goldblum; Andrew Gordon Wilson (ICML, 2024)

Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. - Sanae Lotfi; Yilun Kuang; Marc Finzi; Brandon Amos; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2024)

Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful. - Martin Marek; Sanae Lotfi; Aditya Somasundaram; Andrew Gordon Wilson; Micah Goldblum (CoRR, 2025)