Results 91 to 100 of about 24,904 (254)

Overfitting Reduction in Convex Regression

Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and many other areas. However, it has been empirically observed that convex regression produces inconsistent estimates of convex functions and extremely large subgradients near ...
Liao, Zhiqiang, Dai, Sheng, Lim, Eunji, Kuosmanen, Timo   +3 more
openaire   +2 more sources

Multi-task learning regression via convex clustering

Computational Statistics & Data Analysis
Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL, there are several assumptions for the relationships and methods to incorporate them.
Akira Okazaki, Shuichi Kawano
openaire   +2 more sources

A Scalable Droplet Microfluidic Platform That Overcomes Large‐Cell Constraints for Long‐Term Plant Cell Culture and Embryogenesis Analysis

Advanced Science, EarlyView.
This work presents a large‐cell‐compatible droplet microfluidic platform that overcomes long‐standing barriers in plant cell culture by enabling high‐fidelity encapsulation, novel multilayer 3D droplet incubation for scalable long‐term culture, and support for the full developmental progression of large plant cells.
Arman Mirmiran, Fangchi Shao, Gregory Datto, Tathansh Joshi, Elizabeth Chatt, Yashar Bashirzadeh, Dan Stessman, Lai Wei, Kuangwen Hsieh, Hena Guo, Tza‐Huei Wang   +10 more
wiley   +1 more source

Optimal resource allocation: Convex quantile regression approach

European Journal of Operational Research
Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop quantile allocation models to examine how much the output and productivity could potentially increase if ...
Sheng Dai, Natalia Kuosmanen, Timo Kuosmanen, Juuso Liesiö   +3 more
openaire   +3 more sources

Vine‐Inspired Soft Robotics: Materials, Actuations, and Emerging Applications

Advanced Science, EarlyView.
Vine‐inspired robots grow from the tip, enabling navigation through confined spaces with minimal disturbance. This Review summarizes material and actuation strategies for vine‐inspired growth, key functions including steering, retraction, attachment, branching, and variable stiffness, and emerging applications spanning medical intervention, confined ...
Jiahao Wu, Xingyun Liu, Fangzhou Wang, Huili Ren, Chaojun Zhang, Ziyu Ren, Zunqi Liu, Jing Li   +7 more
wiley   +1 more source

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric

Mathematics
Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting.
Ahmad B. Hassanat, Mohammad Khaled Alqaralleh, Ahmad S. Tarawneh, Khalid Almohammadi, Maha Alamri, Abdulkareem Alzahrani, Ghada A. Altarawneh, Rania Alhalaseh   +7 more
doaj   +1 more source

Achieving the oracle property of OEM with nonconvex penalties

Statistical Theory and Related Fields, 2017
Thepenalised least square estimator of non-convex penalties such as the smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) is highly nonlinear and has many local optima.
Shifeng Xiong, Bin Dai, Peter Z. G. Qian
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

pyDMS: A Python package for the determination of physics‐informed dual‐mode sorption (DMS) parameters

AIChE Journal, EarlyView.
Abstract Sorption in glassy polymer membranes is commonly modeled with the dual‐mode sorption (DMS) model. Fitting the DMS model to sorption isotherms presents challenges, as multiple parameter sets may prove satisfactory. This work presents pyDMS, an open‐source Python package for the computation of DMS parameters obtained via a physics‐informed ...
Brandon C. Tapia, Pablo A. Dean, Jing Ying Yeo, Albert X. Wu, Zachary P. Smith   +4 more
wiley   +1 more source

Regularization Paths for Generalized Linear Models via Coordinate Descent

Journal of Statistical Software, 2010
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman, Trevor Hastie, Rob Tibshirani   +2 more
doaj