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Found 7 papers, 1 papers with code
Improved Rate of First Order Algorithms for Entropic Optimal Transport
To compare, we prove that the computational complexity of the Stochastic Sinkhorn algorithm is $\widetilde{{O}}({n^2}/{\epsilon^2})$, which is slower than our accelerated primal-dual stochastic mirror algorithm.
Covariance Estimators for the ROOT-SGD Algorithm in Online Learning
Our second estimator is a Hessian-free estimator that overcomes the aforementioned limitation.
Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm
Computing the empirical Wasserstein distance in the independence test requires solving this special type of OT problem, where $m=n^2$.
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models
In addition, the Gradient Descent (GD) with a moderate or small step-size converges along the direction that corresponds to the smallest eigenvalue.
Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent
On the other hand, algorithms such as gradient descent and stochastic gradient descent have the implicit regularization property that leads to better performance in terms of the generalization errors.
An Accelerated Stochastic Algorithm for Solving the Optimal Transport Problem
A primal-dual accelerated stochastic gradient descent with variance reduction algorithm (PDASGD) is proposed to solve linear-constrained optimization problems.
Directional Bias Helps Stochastic Gradient Descent to Generalize in Nonparametric Model
This paper studies the Stochastic Gradient Descent (SGD) algorithm in kernel regression.
