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Reducing Discretization Error in the Frank-Wolfe Method
The Frank-Wolfe algorithm is a popular method in structurally constrained machine learning applications, due to its fast per-iteration complexity.
A Multistep Frank-Wolfe Method
The Frank-Wolfe algorithm has regained much interest in its use in structurally constrained machine learning applications.
Accelerating Frank-Wolfe via Averaging Step Directions
The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its fast per-iteration complexity.
Efficient Construction of Nonlinear Models over Normalized Data
For the case of NN, we propose algorithms to train the network taking normalized data as the input.
Learning Continuous-Time Dynamics by Stochastic Differential Networks
Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension.
