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

Master's Research - McGill University
Postdoctorate - McGill University
Master's Research - McGill University
Master's Research - McGill University
PhD - McGill University
Master's Research - McGill University
PhD - McGill University

Publications

4+3 Phases of Compute-Optimal Neural Scaling Laws
Elliot Paquette
Lechao Xiao
Jeffrey Pennington
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
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract
Tomas Gonzalez
Cristobal Guzman
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion
Anna Korba
Peter Bartlett
Mathieu Blondel
Valentin De Bortoli
Arnaud Doucet
Felipe Llinares-López
Quentin Berthet
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion
Anna Korba
Peter Bartlett
Mathieu Blondel
Valentin De Bortoli
Arnaud Doucet
Felipe Llinares-L'opez
Quentin Berthet
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tom'as Gonz'alez
Crist'obal Guzm'an
Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models
Elizabeth Collins-Woodfin
Elliot Paquette
Inbar Seroussi
Only tails matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha
Fabian Pedregosa
Damien Scieur
Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha
Fabian Pedregosa
Damien Scieur
Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties
Elliot Paquette
Ben Adlam
Jeffrey Pennington
Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties
Elliot Paquette
Ben Adlam
Jeffrey Pennington
We develop a stochastic differential equation, called homogenized SGD, for analyzing the dynamics of stochastic gradient descent (SGD) on a … (see more)high-dimensional random least squares problem with
Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis
Bart van Merriënboer
Fabian Pedregosa