6 citations · 12 across the 6 of their papers we have counts for
6 papers
First- and Second-Order Stochastic Adaptive Regularization with Cubics: High Probability Iteration and Sample Complexity
Katya Scheinberg, Miaolan Xie
We present high-probability (and expectation) complexity bounds for two versions of stochastic adaptive regularization methods with cubics (SARC), also known as regularized Newton…
Sample Complexity Analysis for Adaptive Optimization Algorithms with Stochastic Oracles
Billy Jin, Katya Scheinberg, Miaolan Xie
Several classical adaptive optimization algorithms, such as line search and trust region methods, have been recently extended to stochastic settings where function values, gradient…
A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization
Albert S. Berahas, Miaolan Xie, Baoyu Zhou
A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint functio…
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles
Brian Liu, Miaolan Xie, Haoyue Yang +1
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this packa…
ControlBurn: Feature Selection by Sparse Forests
Brian Liu, Miaolan Xie, Madeleine Udell
Tree ensembles distribute feature importance evenly amongst groups of correlated features. The average feature ranking of the correlated group is suppressed, which reduces interpre…
High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles
Billy Jin, Katya Scheinberg, Miaolan Xie
We consider a step search method for continuous optimization under a stochastic setting where the function values and gradients are available only through inexact probabilistic zer…