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Katie Everett

Alumni

Publications

Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
2025-05-22
ArXiv (prépublication)
arxiv.org
arxiv.org
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by dat… (voir plus)a complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.
2025-05-01
arXiv (publié)
doi.org
arxiv.org
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
Sébastien Lachapelle
Pau Rodriguez
Yash Sharma
Katie Everett
Rémi LE PRIOL
Alexandre Lacoste
Simon Lacoste-Julien
2024-01-10
ArXiv (prépublication)
doi.org
arxiv.org