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Found 4 papers, 1 papers with code
Online Sequential Decision-Making with Unknown Delays
Specifically, we introduce a family of Follow the Delayed Regularized Leader algorithms for feedback with full information on the loss function, a family of Delayed Mirror Descent algorithms for feedback with gradient information on the loss function and a family of Simplified Delayed Mirror Descent algorithms for feedback with the value information of the loss function's gradients at corresponding decision points.
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
Due to its simplicity and outstanding ability to generalize, stochastic gradient descent (SGD) is still the most widely used optimization method despite its slow convergence.
SPAN: A Stochastic Projected Approximate Newton Method
SPAN computes the inverse of the Hessian matrix via low-rank approximation and stochastic Hessian-vector products.
Acutum: When Generalization Meets Adaptability
To the best of our knowledge, Acutum is the first adaptive gradient method without second moments.
