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Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Phenomenological theory of variational quantum ground-state preparation

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Sublinear Algorithms for Spectral Graph Clustering

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

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Disparity Between Batches as a Signal for Early Stopping

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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