Stat.ML

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential …
Cite URL arXiv
Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
In this work, we consider solving optimization problems with a stochastic objective and deterministic equality constraints. We propose …
Cite arXiv
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems …
Cite DOI arXiv
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety …
Cite arXiv
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which …
Cite DOI arXiv
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the …
Cite DOI arXiv
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for …
Cite DOI arXiv
The Graph-Based Behavior-Aware Recommendation for Interactive News
Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from …
Cite DOI arXiv
High-dimensional Index Volatility Models via Stein's Identity
We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order …
Cite DOI arXiv
AEGCN: An Autoencoder-Constrained Graph Convolutional Network
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node …
Cite DOI arXiv