Results 31 to 40 of about 4,185 (251)

Structural properties of oracle classes

Information Processing Letters, 2009
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openaire   +4 more sources

Sparse Semi-Functional Partial Linear Single-Index Regression

Proceedings, 2018
The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure is employed for this task.
Silvia Novo, Germán Aneiros, Philippe Vieu   +2 more
doaj   +1 more source

Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data

Mathematics, 2022
Variable selection is often needed in many fields and has been discussed by many authors in various situations. This is especially the case under linear models and when one observes complete data.
Rong Liu, Shishun Zhao, Tao Hu, Jianguo Sun   +3 more
doaj   +1 more source

Oracles for structural properties: The isomorphism problem and public-key cryptography

Journal of Computer and System Sciences, 1992
Relativized computations are known to be a useful tool for assessing the difficulty of answering some important open questions in structural complexity theory. The main result of the paper is a construction of an oracle relative to which -- besides other properties -- all sets complete in the second level of polynomial hierarchy are p-isomorphic, and ...
Steven Homer, Alan L. Selman
openaire   +3 more sources

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Variable selection in finite mixture of median regression models using skew-normal distribution

Statistical Theory and Related Fields, 2023
A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes.
Xin Zeng, Yuanyuan Ju, Liucang Wu
doaj   +1 more source

The Adaptive $\tau$-Lasso: Robustness and Oracle Properties

IEEE Transactions on Signal Processing
This paper introduces a new regularized version of the robust $τ$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates. The resulting estimator, termed adaptive $τ$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty
Emadaldin Mozafari-Majd, Visa Koivunen
openaire   +3 more sources

On oracle property and asymptotic validity of Bayesian generalized method of moments

Journal of Multivariate Analysis, 2016
Statistical inference based on moment conditions and estimating equations is of substantial interest when it is difficult to specify a full probabilistic model. We propose a Bayesian flavored model selection framework based on (quasi-)posterior probabilities from the Bayesian Generalized Method of Moments (BGMM), which allows us to incorporate two ...
Cheng Li 0063, Wenxin Jiang 0003
openaire   +4 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Human-in-the-loop active learning for goal-oriented molecule generation

Journal of Cheminformatics
Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules.
Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski   +8 more
doaj   +1 more source