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ORCIDElliot Paquette
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2020 – today
- 2024
[i10]Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington:
4+3 Phases of Compute-Optimal Neural Scaling Laws. CoRR abs/2405.15074 (2024)- [i9]Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette:
A Clipped Trip: the Dynamics of SGD with Gradient Clipping in High-Dimensions. CoRR abs/2406.11733 (2024) - 2023
- [j5]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) - [j4]Tim Hoheisel, Elliot Paquette:
Uniqueness in Nuclear Norm Minimization: Flatness of the Nuclear Norm Sphere and Simultaneous Polarization. J. Optim. Theory Appl. 197(1): 252-276 (2023) - [i8]Afonso S. Bandeira, Antoine Maillard, Shahar Mendelson, Elliot Paquette:
Fitting an ellipsoid to a quadratic number of random points. CoRR abs/2307.01181 (2023) - [i7]Shurong Lin, Elliot Paquette, Eric D. Kolaczyk:
Differentially Private Linear Regression with Linked Data. CoRR abs/2308.00836 (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
- [c4]Kiwon Lee, Andrew N. Cheng, Elliot Paquette, Courtney Paquette:
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. NeurIPS 2022 - [c3]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) - 2021
- [c2]Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette:
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. COLT 2021: 3548-3626 - [c1]Courtney Paquette, Elliot Paquette:
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. NeurIPS 2021: 9229-9240 - [i3]Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette:
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. CoRR abs/2102.04396 (2021) - [i2]Courtney Paquette, Elliot Paquette:
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. CoRR abs/2106.03696 (2021) - 2020
- [j3]Hoi H. Nguyen, Elliot Paquette:
Surjectivity of near-square random matrices. Comb. Probab. Comput. 29(2): 267-292 (2020) - [i1]Elliot Paquette, Thomas Trogdon:
Universality for the conjugate gradient and MINRES algorithms on sample covariance matrices. CoRR abs/2007.00640 (2020)
2010 – 2019
- 2017
- [j2]Christopher Hoffman, Matthew Kahle, Elliot Paquette:
The Threshold for Integer Homology in Random d-Complexes. Discret. Comput. Geom. 57(4): 810-823 (2017) - 2010
- [j1]Tamás Keleti, Elliot Paquette:
The Trouble with von Koch Curves Built from ngons. Am. Math. Mon. 117(2): 124-137 (2010)
Coauthor Index
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last updated on 2024-10-07 22:20 CEST by the dblp team
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