Nicolas Loizou
20 papers · 2019–2025 · 4 venues · across top CS/AI venues
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Venues
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Research topics
Machine Learning
(17)
Optimization & Theory Β· Machine Learning
(17)
Mathematics & Optimization
(12)
Optimization
(12)
Artificial Intelligence
(6)
Deep Learning
(4)
Core AI
(4)
Optimization & Theory Β· Deep Learning
(4)
Learning Paradigms
(3)
Learning Types
(2)
Reinforcement Learning
(1)
Methods
(1)
Keywords
stochastic gradient descent
(8)
variational inequality
(7)
convergence analysis
(6)
convergence rate
(5)
stochastic optimization
(5)
convergence guarantee
(4)
extragradient method
(4)
distributed learning
(3)
stochastic extragradient
(3)
min-max optimization
(3)
minimax optimization
(3)
polyak stepsize
(2)
bilinear game
(2)
gossip algorithm
(2)
adaptive step size
(2)
non-convex optimization
(2)
neural network optimization
(2)
smooth game
(2)
decentralized optimization
(1)
few-shot learning
(1)
Papers
Sharpness-Aware Minimization: General Analysis and Improved Rates
ICLR 2025
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
ICLR 2025
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
ICLR 2024
Enhancing vision-language models for medical imaging: bridging the 3D gap with innovative slice selection
NeurIPS 2024
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
NeurIPS 2024
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities
AISTATS 2024
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
ICLR 2023
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
NeurIPS 2023
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
AISTATS 2023
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution
NeurIPS 2022
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity
AISTATS 2022
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
AISTATS 2022
Stochastic Extragradient: General Analysis and Improved Rates
AISTATS 2022
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
AISTATS 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
NeurIPS 2021
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
AISTATS 2021
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
ICML 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games
ICML 2020
SGD: General Analysis and Improved Rates
ICML 2019
Stochastic Gradient Push for Distributed Deep Learning
ICML 2019