Search Results for author: Sanae Lotfi

Found 6 papers, 4 papers with code

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization

1 code implementation24 Nov 2022 Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson

While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works.

Generalization Bounds Transfer Learning

Bayesian Model Selection, the Marginal Likelihood, and Generalization

1 code implementation23 Feb 2022 Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson

We provide a partial remedy through a conditional marginal likelihood, which we show is more aligned with generalization, and practically valuable for large-scale hyperparameter learning, such as in deep kernel learning.

Model Selection Neural Architecture Search

Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning

no code implementations29 Nov 2021 Sanae Lotfi, Tiphaine Bonniot de Ruisselet, Dominique Orban, Andrea Lodi

In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning.

BIG-bench Machine Learning Stochastic Optimization

Dangers of Bayesian Model Averaging under Covariate Shift

1 code implementation NeurIPS 2021 Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson

Approximate Bayesian inference for neural networks is considered a robust alternative to standard training, often providing good performance on out-of-distribution data.

Bayesian Inference

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling

1 code implementation25 Feb 2021 Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson

In this paper, we show that there are mode-connecting simplicial complexes that form multi-dimensional manifolds of low loss, connecting many independently trained models.

Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation

no code implementations10 Dec 2020 Sanae Lotfi, Tiphaine Bonniot de Ruisselet, Dominique Orban, Andrea Lodi

We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the Hessian approximation to balance its quality and conditioning.


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