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Courtney Paquette
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2020 – today
- 2024
- [c7]Tomás González, Cristóbal Guzmán, Courtney Paquette:
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract. COLT 2024: 1982 - [i9]Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet:
Implicit Diffusion: Efficient Optimization through Stochastic Sampling. CoRR abs/2402.05468 (2024) - [i8]Tomás González, Cristóbal Guzmán, Courtney Paquette:
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems. CoRR abs/2403.02912 (2024) - [i7]Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington:
4+3 Phases of Compute-Optimal Neural Scaling Laws. CoRR abs/2405.15074 (2024) - 2023
- [j4]Courtney Paquette, Bart van Merriënboer, Elliot Paquette, Fabian Pedregosa:
Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis. Found. Comput. Math. 23(2): 597-673 (2023) - [i6]Elizabeth Collins-Woodfin
, Courtney Paquette, Elliot Paquette, Inbar Seroussi:
Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models. CoRR abs/2308.08977 (2023) - 2022
- [c6]Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette:
Only tails matter: Average-Case Universality and Robustness in the Convex Regime. ICML 2022: 4474-4491 - [c5]Kiwon Lee, Andrew N. Cheng, Elliot Paquette, Courtney Paquette:
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. NeurIPS 2022 - [c4]Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. NeurIPS 2022 - [i5]Kiwon Lee, Andrew N. Cheng, Courtney Paquette, Elliot Paquette:
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. CoRR abs/2206.01029 (2022) - [i4]Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. CoRR abs/2206.07252 (2022) - [i3]Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette:
Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime. CoRR abs/2206.09901 (2022) - 2021
- [c3]Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette:
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. COLT 2021: 3548-3626 - [c2]Courtney Paquette, Elliot Paquette:
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. NeurIPS 2021: 9229-9240 - [i2]Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette:
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. CoRR abs/2102.04396 (2021) - [i1]Courtney Paquette, Elliot Paquette:
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. CoRR abs/2106.03696 (2021) - 2020
- [j3]Courtney Paquette, Katya Scheinberg
A Stochastic Line Search Method with Expected Complexity Analysis. SIAM J. Optim. 30(1): 349-376 (2020)
2010 – 2019
- 2019
- [j2]Dmitriy Drusvyatskiy
Efficiency of minimizing compositions of convex functions and smooth maps. Math. Program. 178(1-2): 503-558 (2019) - 2018
- [j1]Damek Davis, Dmitriy Drusvyatskiy
Subgradient Methods for Sharp Weakly Convex Functions. J. Optim. Theory Appl. 179(3): 962-982 (2018) - [c1]Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui:
Catalyst for Gradient-based Nonconvex Optimization. AISTATS 2018: 613-622
Coauthor Index
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