Courtney Paquette

According to our database, Courtney Paquette authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

4+3 Phases of Compute-Optimal Neural Scaling Laws.

Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington

CoRR, 2024

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems.

Tomás González, Cristóbal Guzmán, Courtney Paquette

CoRR, 2024

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

CoRR, 2024

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract.

Tomás González, Cristóbal Guzmán, Courtney Paquette

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis.

Courtney Paquette, Bart van Merriënboer, Elliot Paquette, Fabian Pedregosa

Found. Comput. Math., April, 2023

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

CoRR, 2023

2022

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions.

Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions.

Kiwon Lee, Andrew N. Cheng, Elliot Paquette, Courtney Paquette

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Only tails matter: Average-Case Universality and Robustness in the Convex Regime.

Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette

Proceedings of the International Conference on Machine Learning, 2022

2021

Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models.

Courtney Paquette, Elliot Paquette

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality.

Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette

Proceedings of the Conference on Learning Theory, 2021

2020

A Stochastic Line Search Method with Expected Complexity Analysis.

Courtney Paquette, Katya Scheinberg

SIAM J. Optim., 2020

2019

Efficiency of minimizing compositions of convex functions and smooth maps.

Dmitriy Drusvyatskiy, Courtney Paquette

Math. Program., 2019

2018

Subgradient Methods for Sharp Weakly Convex Functions.

Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee, Courtney Paquette

J. Optim. Theory Appl., 2018

Catalyst for Gradient-based Nonconvex Optimization.

Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018