Elliot Paquette

29 papers A* 8Journal 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Logarithmic-time Schedules for Scaling Language Models with Momentum. CoRR Damien Ferbach, Courtney Paquette, Gauthier Gidel, Katie Everett, Elliot Paquette
2026	J	`jnl`	Power-Law Spectrum of the Random Feature Model. CoRR Elliot Paquette, Ke Liang Xiao, Yizhe Zhu
2025	J	`jnl`	Dimension-adapted Momentum Outscales SGD. CoRR Damien Ferbach, Katie Everett, Gauthier Gidel, Elliot Paquette, Courtney Paquette
2025	A*	`conf`	Exact risk curves of signSGD in High-Dimensions: quantifying preconditioning and noise-compression effects. ICML Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette
2025	J	`jnl`	High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares. CoRR Shurong Lin, Eric D. Kolaczyk, Adam Smith, Elliot Paquette
2025	J	`jnl`	Homogenization of SGD in high-dimensions: exact dynamics and generalization properties. Math. Program. Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
2025	A*	`conf`	To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions. ICLR Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette
2024	A*	`conf`	4+3 Phases of Compute-Optimal Neural Scaling Laws. NeurIPS Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington
2024	J	`jnl`	4+3 Phases of Compute-Optimal Neural Scaling Laws. CoRR Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington
2024	J	`jnl`	A Clipped Trip: the Dynamics of SGD with Gradient Clipping in High-Dimensions. CoRR Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette
2024	J	`jnl`	Exact Risk Curves of signSGD in High-Dimensions: Quantifying Preconditioning and Noise-Compression Effects. CoRR Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette
2024	A*	`conf`	The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms. NeurIPS Elizabeth Collins-Woodfin, Inbar Seroussi, Begoña García Malaxechebarría, Andrew W. Mackenzie, Elliot Paquette, Courtney Paquette
2023	J	`jnl`	Differentially Private Linear Regression with Linked Data. CoRR Shurong Lin, Elliot Paquette, Eric D. Kolaczyk
2023	J	`jnl`	Fitting an ellipsoid to a quadratic number of random points. CoRR Afonso S. Bandeira, Antoine Maillard, Shahar Mendelson, Elliot Paquette
2023	J	`jnl`	Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis. Found. Comput. Math. Courtney Paquette, Bart van Merriënboer, Elliot Paquette, Fabian Pedregosa
2023	J	`jnl`	Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models. CoRR Elizabeth Collins-Woodfin, Courtney Paquette, Elliot Paquette, Inbar Seroussi
2023	J	`jnl`	Uniqueness in Nuclear Norm Minimization: Flatness of the Nuclear Norm Sphere and Simultaneous Polarization. J. Optim. Theory Appl. Tim Hoheisel, Elliot Paquette
2022	A*	`conf`	Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. NeurIPS Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
2022	J	`jnl`	Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. CoRR Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
2022	A*	`conf`	Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. NeurIPS Kiwon Lee, Andrew N. Cheng, Elliot Paquette, Courtney Paquette
2022	J	`jnl`	Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions. CoRR Kiwon Lee, Andrew N. Cheng, Courtney Paquette, Elliot Paquette
2021	A*	`conf`	Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. NeurIPS Courtney Paquette, Elliot Paquette
2021	J	`jnl`	Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models. CoRR Courtney Paquette, Elliot Paquette
2021	A*	`conf`	SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. COLT Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette
2021	J	`jnl`	SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. CoRR Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette
2020	J	`jnl`	Surjectivity of near-square random matrices. Comb. Probab. Comput. Hoi H. Nguyen, Elliot Paquette
2020	J	`jnl`	Universality for the conjugate gradient and MINRES algorithms on sample covariance matrices. CoRR Elliot Paquette, Thomas Trogdon
2017	J	`jnl`	The Threshold for Integer Homology in Random d-Complexes. Discret. Comput. Geom. Christopher Hoffman, Matthew Kahle, Elliot Paquette
2010	J	`jnl`	The Trouble with von Koch Curves Built from Am. Math. Mon. Tamás Keleti, Elliot Paquette