What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
Ming Yuan focuses on Mathematical optimization, Estimator, Applied mathematics, Model selection and Lasso.
His work carried out in the field of Mathematical optimization brings together such families of science as Regression analysis, Tensor product of Hilbert spaces, Feature selection and Regularization.
Ming Yuan combines subjects such as Nonparametric statistics, Quantile, Econometrics, Minimax and Algorithm with his study of Estimator.
His study looks at the intersection of Applied mathematics and topics like Least squares with Coefficient matrix, Linear model, Coefficient of determination and Shrinkage estimator.
Ming Yuan studies Lasso, focusing on Elastic net regularization in particular.
He works mostly in the field of Focus, limiting it down to concerns involving Machine learning and, occasionally, Artificial intelligence.
His most cited work include:
- Model selection and estimation in regression with grouped variables (5399 citations)
- Model selection and estimation in the Gaussian graphical model (1293 citations)
- Composite quantile regression and the oracle model selection theory (356 citations)
What are the main themes of his work throughout his whole career to date?
Ming Yuan mostly deals with Mathematical optimization, Applied mathematics, Estimator, Artificial intelligence and Statistics.
His Mathematical optimization research includes themes of Regularization, Monte Carlo method, Reproducing kernel Hilbert space and Lasso.
While the research belongs to areas of Lasso, Ming Yuan spends his time largely on the problem of Feature selection, intersecting his research to questions surrounding Data mining.
His studies deal with areas such as Nonparametric statistics, Rate of convergence, Estimation theory, Algorithm and Mean squared error as well as Estimator.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
Ming Yuan interconnects Regression analysis and Model selection in the investigation of issues within Linear regression.
He most often published in these fields:
- Mathematical optimization (23.33%)
- Applied mathematics (22.22%)
- Estimator (21.11%)
What were the highlights of his more recent work (between 2018-2021)?
- Estimator (21.11%)
- Minimax (11.67%)
- Applied mathematics (22.22%)
In recent papers he was focusing on the following fields of study:
Ming Yuan mainly investigates Estimator, Minimax, Applied mathematics, Algorithm and Nanophotonics.
Ming Yuan usually deals with Estimator and limits it to topics linked to Multivariate statistics and Singular value decomposition.
His research integrates issues of Structure, Iterated function, Group and Dimension in his study of Minimax.
His work deals with themes such as Kernel embedding of distributions, Upper and lower bounds, Tensor and Dimensionality reduction, which intersect with Applied mathematics.
As a member of one scientific family, Ming Yuan mostly works in the field of Algorithm, focusing on Inference and, on occasion, Statistical inference, Consistent estimator and Matrix completion.
Ming Yuan merges many fields, such as Mathematical optimization and Simple, in his writings.
Between 2018 and 2021, his most popular works were:
- Efficient multivariate entropy estimation via $k$-nearest neighbour distances (46 citations)
- Convex regularization for high-dimensional multiresponse tensor regression (36 citations)
- Nanophotonic media for artificial neural inference (32 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His primary areas of study are Applied mathematics, Minimax, Tensor, Estimator and Algorithm.
The Applied mathematics study combines topics in areas such as Upper and lower bounds, Regression and Dimensionality reduction.
The various areas that Ming Yuan examines in his Upper and lower bounds study include Entropy estimation, Entropy, Efficient estimator, Mean squared error and Independent and identically distributed random variables.
His Minimax research incorporates themes from Subspace topology, Intrinsic dimension, Statistical model and Convex set.
Ming Yuan focuses mostly in the field of Tensor, narrowing it down to topics relating to Gradient descent and, in certain cases, Optimization problem and Multilinear map.
His study in Algorithm is interdisciplinary in nature, drawing from both Norm and Statistical inference.
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