Results 131 to 140 of about 25,397 (329)
Truthful Linear Regression [PDF]
, 2015 We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy ...
Cummings, Rachel +2 more
core +3 more sources
Advanced Intelligent Discovery, EarlyView.This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
Advanced Intelligent Discovery, EarlyView.As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
The Augmented Synthetic Control Method
, 2020 The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment ...
Ben-Michael, Eli +2 more
core
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Bayesian Analyses of Ridge Regression Prooblems
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2017 A Bayesian formulation of the ridge regression problem is considerd, which derives from a direct specification of prior informations about parameters of general linear regression model when data suffer from a high degree of multicollinearity.A new ...
H. M. Gorgees
doaj
Advanced Intelligent Discovery, EarlyView.This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Generalized ridge estimator in negative binomial regression model [PDF]
, 2021 Nadwa Khazaal Rashad +2 more
openalex +1 more source