41 citations · 80 across the 19 of their papers we have counts for
20 papers
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
Ilgee Hong, Sen Na, Michael W. Mahoney +1
We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety of applications in machine learning and engi…
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
Yuchen Fang, Sen Na, Michael W. Mahoney +1
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems with stochastic objectives and deterministic eq…
Near-Optimal Performance of Stochastic Model Predictive Control
Sungho Shin, Sen Na, Mihai Anitescu
This article presents a dynamic regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative un…
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
Sen Na, Michael W. Mahoney
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential Quadratic Programming (StoSQP) method to solv…
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
Sen Na, Michał Dereziński, Michael W. Mahoney
We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the algorithm is given an oracle access to a stochastic…
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
Sen Na, Mihai Anitescu, Mladen Kolar
We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which emerge in numerous applications including finance,…
A Fast Temporal Decomposition Procedure for Long-horizon Nonlinear Dynamic Programming
Sen Na, Mihai Anitescu, Mladen Kolar
We propose a fast temporal decomposition procedure for solving long-horizon nonlinear dynamic programs. The core of the procedure is sequential quadratic programming (SQP) that uti…
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
Sen Na, Mihai Anitescu, Mladen Kolar
We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for the objective that its evaluation, gradient, a…
AEGCN: An Autoencoder-Constrained Graph Convolutional Network
Mingyuan Ma, Sen Na, Hongyu Wang
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its…
Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
You-Lin Chen, Sen Na, Mladen Kolar
We study the convergence of accelerated stochastic gradient descent for strongly convex objectives under the growth condition, which states that the variance of stochastic gradient…
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
Sen Na, Sungho Shin, Mihai Anitescu +1
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domai…
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Sen Na, Yuwei Luo, Zhuoran Yang +2
Graph representation learning is a ubiquitous task in machine learning where the goal is to embed each vertex into a low-dimensional vector space. We consider the bipartite graph a…
Superconvergence of Online Optimization for Model Predictive Control
Sen Na, Mihai Anitescu
We develop a one-Newton-step-per-horizon, online, lag-, model predictive control (MPC) algorithm for solving discrete-time, equality-constrained, nonlinear dynamic programs. Bas…
Exponential Decay in the Sensitivity Analysis of Nonlinear Dynamic Programming
Sen Na, Mihai Anitescu
In this paper, we study the sensitivity of discrete-time dynamic programs with nonlinear dynamics and objective to perturbations in the initial conditions and reference parameters.…
Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity
Sen Na, Mladen Kolar, Oluwasanmi Koyejo
Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific i…
The Graph-Based Behavior-Aware Recommendation for Interactive News
Mingyuan Ma, Sen Na, Hongyu Wang +2
Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors…
High-dimensional Index Volatility Models via Stein's Identity
Sen Na, Mladen Kolar
We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order Stein's identities, we develop methods that a…
High-dimensional Varying Index Coefficient Models via Stein's Identity
Sen Na, Zhuoran Yang, Zhaoran Wang +1
We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link…
Scalable Peaceman-Rachford Splitting Method with Proximal Terms
Sen Na, Mingyuan Ma, Mladen Kolar
Along with developing of Peaceman-Rachford Splittling Method (PRSM), many batch algorithms based on it have been studied very deeply. But almost no algorithm focused on the perform…
Sparse Learning with Semi-Proximal-Based Strictly Contractive Peaceman-Rachford Splitting Method
Sen Na, Cho-Jui Hsieh
Minimizing sum of two functions under a linear constraint is what we called splitting problem. This convex optimization has wide applications in machine learning problems, such as…