@article{liu_chi_lange_2022, title={A Sharper Computational Tool for L2E Regression}, volume={10}, ISSN={["1537-2723"]}, url={https://doi.org/10.1080/00401706.2022.2118172}, DOI={10.1080/00401706.2022.2118172}, abstractNote={Abstract Building on previous research of Chi and Chi, this article revisits estimation in robust structured regression under the criterion. We adopt the majorization-minimization (MM) principle to design a new algorithm for updating the vector of regression coefficients. Our sharp majorization achieves faster convergence than the previous alternating proximal gradient descent algorithm by Chi and Chi. In addition, we reparameterize the model by substituting precision for scale and estimate precision via a modified Newton’s method. This simplifies and accelerates overall estimation. We also introduce distance-to-set penalties to enable constrained estimation under nonconvex constraint sets. This tactic also improves performance in coefficient estimation and structure recovery. Finally, we demonstrate the merits of our improved tactics through a rich set of simulation examples and a real data application.}, journal={TECHNOMETRICS}, author={Liu, Xiaoqian and Chi, Eric C. and Lange, Kenneth}, year={2022}, month={Oct} }
 @article{liu_chi_2022, title={Revisiting convexity-preserving signal recovery with the linearly involved GMC penalty}, volume={156}, ISSN={["1872-7344"]}, url={https://doi.org/10.1016/j.patrec.2022.02.004}, DOI={10.1016/j.patrec.2022.02.004}, abstractNote={• A new method for setting the matrix parameter in the linearly involved GMC is proposed. • An alternative algorithm is presented to solve the linear involved convexity-preserving model. • Two properties of the solution path are proved to help with tuning parameter selection. The generalized minimax concave (GMC) penalty is a newly proposed regularizer that can maintain the convexity of the objective function. This paper deals with signal recovery with the linearly involved GMC penalty. First, we propose a new method to set the matrix parameter in the penalty via solving a feasibility problem. The new method possesses appealing advantages over the existing method. Second, we recast the linearly involved GMC model as a saddle-point problem and use the primal-dual hybrid gradient (PDHG) algorithm to compute the solution. Another important work in this paper is that we provide guidance on the tuning parameter selection by proving desirable properties of the solution path. Finally, we apply the linearly involved GMC penalty to 1-D signal recovery and matrix regression. Numerical results show that the linearly involved GMC penalty can obtain better recovery performance and preserve the signal structure more successfully in comparison with the total variation (TV) regularizer.}, journal={PATTERN RECOGNITION LETTERS}, publisher={Elsevier BV}, author={Liu, Xiaoqian and Chi, Eric C.}, year={2022}, month={Apr}, pages={60–66} }
 @article{liu_vardhan_wen_das_randles_chi_2021, title={An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions}, ISSN={["1558-4615"]}, url={http://dx.doi.org/10.1109/embc46164.2021.9631082}, DOI={10.1109/EMBC46164.2021.9631082}, abstractNote={Coronary bifurcation lesions are a leading cause of Coronary Artery Disease (CAD). Despite its prevalence, coronary bifurcation lesions remain difficult to treat due to our incomplete understanding of how various features of lesion anatomy synergistically disrupt normal hemodynamic flow. In this work, we employ an interpretable machine learning algorithm, the Classification and Regression Tree (CART), to model the impact of these geometric features on local hemodynamic quantities. We generate a synthetic arterial database via computational fluid dynamic simulations and apply the CART approach to predict the time averaged wall shear stress (TAWSS) at two different locations within the cardiac vasculature. Our experimental results show that CART can estimate a simple, interpretable, yet accurately predictive nonlinear model of TAWSS as a function of such features.Clinical relevance— The fitted tree models have the potential to refine predictions of disturbed hemodynamic flow based on an individual’s cardiac and lesion anatomy and consequently makes progress towards personalized treatment planning for CAD patients.}, journal={2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)}, publisher={IEEE}, author={Liu, Xiaoqian and Vardhan, Madhurima and Wen, Qinrou and Das, Arpita and Randles, Amanda and Chi, Eric C.}, year={2021}, pages={4432–4435} }