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On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Universal and adaptive methods for robust stochastic optimization

Localizing Unsynchronized Sensors With Unknown Sources

Stochastic homogenization of degenerate integral functionals with linear growth

Stochastic distributed learning with gradient quantization and double-variance reduction

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

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

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

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Stochastic homogenization of degenerate integral functionals with linear growth

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

Localizing Unsynchronized Sensors With Unknown Sources

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

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

Universal and adaptive methods for robust stochastic optimization

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

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Stochastic distributed learning with gradient quantization and double-variance reduction