Courtney Paquette
10 papers · 2018–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π Cross-Pollinator (15) πΊοΈ Taxonomy Completionist (15) π§ Keyword Pioneer π Conference Polyglot (4) π Academic Marathon (7)
π
Interdisciplinary Bridge
π
Century Club
(10)
β
The Questioner
Conferences
NIPS (5)
AISTATS (2)
COLT (2)
ICML (1)
Top co-authors
Keywords
stochastic gradient descent
(5)
nesterov acceleration
(2)
least square
(2)
convergence rate
(2)
random matrix theory
(2)
convex optimization
(2)
momentum method
(2)
convergence analysis
(1)
adaptive learning rate
(1)
stochastic gradient
(1)
mirror descent
(1)
mini-batch optimization
(1)
sparse matrix factorization
(1)
saddle-point problems
(1)
high-dimensional analysis
(1)
nonconvex optimization
(1)
implicit regularization
(1)
gradient descent
(1)
stochastic differential equation
(1)
phase transition
(1)
Papers
Implicit Diffusion: Efficient optimization through stochastic sampling
AISTATS 2025
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract
COLT 2024
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
NIPS 2024
4+3 Phases of Compute-Optimal Neural Scaling Laws
NIPS 2024
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions
NIPS 2022
Only tails matter: Average-Case Universality and Robustness in the Convex Regime
ICML 2022
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
NIPS 2022
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models
NIPS 2021
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality
COLT 2021
Catalyst for Gradient-based Nonconvex Optimization
AISTATS 2018