Sanae Lotfi

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PhD student at New York University

I am a PhD student at the Center for Data Science at NYU and a DeepMind fellow, advised by Professor Andrew Gordon Wilson. I am currently interested in understanding and quantifying the generalization of deep learning models. More broadly, my research interests include out-of-distribution generalization in deep learning, statistical learning theory and inference, Bayesian learning, probabilistic modeling, large-scale optimization, and loss surface analysis.

Prior to NYU, I obtained a master’s degree in applied mathematics from Polytechnique Montreal. I was fortunate to work there with Professors Andrea Lodi and Dominique Orban to design stochastic first- and second-order algorithms with compelling theoretical and empirical properties for machine learning and large-scale optimization. I also hold an engineering degree in general engineering and applied mathematics from CentraleSupélec.


You can contact me at sl8160@nyu.edu

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Publications

Dangers of Bayesian Model Averaging under Covariate Shift
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
Neural Information Processing Systems (NeurIPS), 2021
[arxiv, code]

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 Foong, Sanae Lotfi, Sebastian Farquhar
NeurIPS 2021 Competition
[pdf]

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2021
[arxiv, code]

Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
Sanae Lotfi, Tiphaine B. de Ruisselet, Dominique Orban, Andrea Lodi
NeurIPS Optimization for Machine Learning Workshop, 2020
Spotlight presentation
[arxiv]

Stochastic First and Second Order Optimization Methods for Machine Learning
Sanae Lotfi
Master’s Thesis, 2020
Polytechnique Montreal