Catalyst for Gradient-based Nonconvex Optimization. - Courtney Paquette; Hongzhou Lin; Dmitriy Drusvyatskiy; Julien Mairal; Zaïd Harchaoui (AISTATS, 2018)

Subgradient Methods for Sharp Weakly Convex Functions. - Damek Davis; Dmitriy Drusvyatskiy; Kellie J. MacPhee; Courtney Paquette (J. Optim. Theory Appl., 2018)

Efficiency of minimizing compositions of convex functions and smooth maps. - Dmitriy Drusvyatskiy; Courtney Paquette (Math. Program., 2019)

A Stochastic Line Search Method with Expected Complexity Analysis. - Courtney Paquette; Katya Scheinberg (SIAM J. Optim., 2020)

SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. - Courtney Paquette; Kiwon Lee; Fabian Pedregosa; Elliot Paquette (COLT, 2021)

Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. - Courtney Paquette; Elliot Paquette (NeurIPS, 2021)

Only tails matter: Average-Case Universality and Robustness in the Convex Regime. - Leonardo Cunha; Gauthier Gidel; Fabian Pedregosa; Damien Scieur; Courtney Paquette (ICML, 2022)

Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. - Kiwon Lee; Andrew N. Cheng; Elliot Paquette; Courtney Paquette (NeurIPS, 2022)

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. - Courtney Paquette; Elliot Paquette; Ben Adlam; Jeffrey Pennington (NeurIPS, 2022)

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)

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., 2023)

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract. - Tomás González; Cristóbal Guzmán; Courtney Paquette (COLT, 2024)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms. - Elizabeth Collins-Woodfin; Inbar Seroussi; Begoña García Malaxechebarría; Andrew W. Mackenzie; Elliot Paquette; Courtney Paquette (NeurIPS, 2024)

4+3 Phases of Compute-Optimal Neural Scaling Laws. - Elliot Paquette; Courtney Paquette; Lechao Xiao; Jeffrey Pennington (NeurIPS, 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 L. Bartlett; Mathieu Blondel; Valentin De Bortoli; Arnaud Doucet; Felipe Llinares-López; Courtney Paquette; Quentin Berthet (AISTATS, 2025)

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models. - Alexander B. Atanasov; Blake Bordelon; Jacob A. Zavatone-Veth; Courtney Paquette; Cengiz Pehlevan (CoRR, 2025)

Dimension-adapted Momentum Outscales SGD. - Damien Ferbach; Katie Everett; Gauthier Gidel; Elliot Paquette; Courtney Paquette (CoRR, 2025)