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Kimon Antonakopoulos

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Kimon Antonakopoulos

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On the Generalization of Stochastic Gradient Descent with Momentum

Improving SAM Requires Rethinking its Optimization Formulation

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

ADAGRAD Avoids Saddle Points

ADAGRAD Avoids Saddle Points

Sifting through the Noise: Universal First-Order Methods for Stochastic Variational Inequalities

On the Generalization of Stochastic Gradient Descent with Momentum

Improving SAM Requires Rethinking its Optimization Formulation

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

ADAGRAD Avoids Saddle Points

ADAGRAD Avoids Saddle Points

Sifting through the Noise: Universal First-Order Methods for Stochastic Variational Inequalities

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization