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

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Enabling Uncertainty Estimation in Iterative Neural Networks

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Proximal Point Imitation Learning

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

A first-order primal-dual method with adaptivity to local smoothness

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

A first-order primal-dual method with adaptivity to local smoothness

Enabling Uncertainty Estimation in Iterative Neural Networks

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Proximal Point Imitation Learning

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent