Portrait of Courtney Paquette

Courtney Paquette

Associate Academic Member
Canada CIFAR AI Chair
Assistant Professor, McGill University, Department of Mathematics and Statistics
Research Scientist, Google Brain
Research Topics
Optimization

Biography

Courtney Paquette is an assistant professor at McGill University and a Canada CIFAR AI Chair at Mila – Quebec Artificial Intelligence Institute.

Her research focuses on designing and analyzing algorithms for large-scale optimization problems, motivated by applications in data science.

She received her PhD in mathematics from the University of Washington (2017), held postdoctoral positions at Lehigh University (2017–2018) and the University of Waterloo (NSF postdoctoral fellowship, 2018–2019), and was a research scientist at Google Brain in Montréal (2019–2020).

Current Students

Matt Chaubet
Master's Research - McGill University
Elizabeth Collins-Woodfin
Postdoctorate - McGill University
Website
Damien Ferbach
PhD - Université de Montréal
Principal supervisor :
Gauthier Gidel
Website
Google Scholar
Andrew Mackenzie
Master's Research - McGill University
Website
Github
Noah Marshall
PhD - McGill University
Principal supervisor :
Adam M. Oberman
Yixi Wang Wang
Master's Research - McGill University
wyixi3345@gmail.com
Github
Kevin Xiao
PhD - McGill University

Publications

Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
2025-05-22
ArXiv (preprint)
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… (see more)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.
2025-05-22
ArXiv (preprint)
arxiv.org
arxiv.org
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion
Anna Korba
Peter Bartlett
Mathieu Blondel
Valentin De Bortoli
Arnaud Doucet
Felipe Llinares-López
Courtney Paquette
Quentin Berthet
2025-01-22
aistats.org/AISTATS/2025/Conference (oral)
doi.org
openreview.net
High Dimensional First Order Mini-Batch Algorithms on Quadratic Problems
Andrew Nicholas Cheng
Kiwon Lee
Courtney Paquette
We analyze the dynamics of general mini-batch first order algorithms on the …
2024-10-10
NeurIPS.cc/2024/Workshop/OPT (published)
openreview.net
openreview.net
4+3 Phases of Compute-Optimal Neural Scaling Laws
Elliot Paquette
Courtney Paquette
Lechao Xiao
Jeffrey Pennington
2024-09-25
NeurIPS.cc/2024/Conference (spotlight)
openreview.net
openreview.net
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
Elizabeth Collins-Woodfin
Inbar Seroussi
Begoña García Malaxechebarría
Andrew Mackenzie
Elliot Paquette
Courtney Paquette
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract
Tomas Gonzalez
Cristobal Guzman
Courtney Paquette
2024-06-30
Proceedings of Thirty Seventh Conference on Learning Theory (published)
proceedings.mlr.press
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion
Anna Korba
Peter Bartlett
Mathieu Blondel
Valentin De Bortoli
Arnaud Doucet
Felipe Llinares-L'opez
Courtney Paquette
Quentin Berthet
2024-02-08
ArXiv (preprint)
doi.org
arxiv.org
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tom'as Gonz'alez
Crist'obal Guzm'an
Courtney Paquette
2024-01-01
COLT (published)
doi.org
arxiv.org
Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models
Elizabeth Collins-Woodfin
Courtney Paquette
Elliot Paquette
Inbar Seroussi
2023-08-17
ArXiv (preprint)
doi.org
arxiv.org
Only tails matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha
Gauthier Gidel
Fabian Pedregosa
Damien Scieur
Courtney Paquette
2022-06-28
Proceedings of the 39th International Conference on Machine Learning (published)
doi.org
openreview.net
Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha
Gauthier Gidel
Fabian Pedregosa
Damien Scieur
Courtney Paquette
2022-06-20
ArXiv (preprint)
doi.org
openreview.net