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Courtney Paquette
Courtney Paquette
Google Research, Brain Team
Verified email at u.washington.edu - Homepage
Title
Cited by
Cited by
Year
Efficiency of minimizing compositions of convex functions and smooth maps
D Drusvyatskiy, C Paquette
Mathematical Programming 178, 503-558, 2019
1982019
The nonsmooth landscape of phase retrieval
D Davis, D Drusvyatskiy, C Paquette
IMA Journal of Numerical Analysis 40 (4), 2652-2695, 2020
872020
Subgradient methods for sharp weakly convex functions
D Davis, D Drusvyatskiy, KJ MacPhee, C Paquette
Journal of Optimization Theory and Applications 179, 962-982, 2018
862018
A stochastic line search method with expected complexity analysis
C Paquette, K Scheinberg
SIAM Journal on Optimization 30 (1), 349-376, 2020
692020
Catalyst for gradient-based nonconvex optimization
C Paquette, H Lin, D Drusvyatskiy, J Mairal, Z Harchaoui
International Conference on Artificial Intelligence and Statistics, 613-622, 2018
512018
A stochastic line search method with convergence rate analysis
C Paquette, K Scheinberg
arXiv preprint arXiv:1807.07994, 2018
472018
Catalyst acceleration for gradient-based non-convex optimization
C Paquette, H Lin, D Drusvyatskiy, J Mairal, Z Harchaoui
arXiv preprint arXiv:1703.10993, 2017
412017
Variational analysis of spectral functions simplified
D Drusvyatskiy, C Kempton
arXiv preprint arXiv:1506.05170, 2015
212015
SGD in the large: Average-case analysis, asymptotics, and stepsize criticality
C Paquette, K Lee, F Pedregosa, E Paquette
Conference on Learning Theory, 3548-3626, 2021
182021
Halting time is predictable for large models: A universality property and average-case analysis
C Paquette, B van MerriŽnboer, E Paquette, F Pedregosa
arXiv preprint arXiv:2006.04299, 2020
162020
Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties
C Paquette, E Paquette, B Adlam, J Pennington
arXiv preprint arXiv:2205.07069, 2022
122022
Dynamics of stochastic momentum methods on large-scale, quadratic models
C Paquette, E Paquette
Advances in Neural Information Processing Systems 34, 9229-9240, 2021
112021
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
C Paquette, E Paquette, B Adlam, J Pennington
Advances in Neural Information Processing Systems 35, 35984-35999, 2022
102022
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions
K Lee, A Cheng, E Paquette, C Paquette
Advances in Neural Information Processing Systems 35, 36944-36957, 2022
52022
Only tails matter: Average-Case Universality and Robustness in the Convex Regime
L Cunha, G Gidel, F Pedregosa, D Scieur, C Paquette
International Conference on Machine Learning, 4474-4491, 2022
52022
Potential-based analyses of first-order methods for constrained and composite optimization
C Paquette, S Vavasis
arXiv preprint arXiv:1903.08497, 2019
22019
Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models
E Collins-Woodfin, C Paquette, E Paquette, I Seroussi
arXiv preprint arXiv:2308.08977, 2023
12023
A termination criterion for stochastic gradient descent for binary classification
S Baghal, C Paquette, SA Vavasis
arXiv preprint arXiv:2003.10312, 2020
2020
Algorithms for stochastic problems lacking convexity or smoothness
C Paquette
2019
Minimization of convex composites
C Paquette
2017
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