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- Martin J. Wainwright

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
79
Citations
35,194
206
World Ranking
453
National Ranking
267

Mathematics
D-index
88
Citations
37,650
223
World Ranking
38
National Ranking
25

2014 - COPSS Presidents' Award For fundamental and groundbreaking contributions to high-dimensional statistics, graphical modeling, machine learning, optimization and algorithms covering deep and elegant mathematical analysis as well as new methodology with wide-ranging implications for numerous applications.

2005 - Fellow of Alfred P. Sloan Foundation

- Statistics
- Artificial intelligence
- Machine learning

Martin J. Wainwright spends much of his time researching Mathematical optimization, Combinatorics, Estimator, Applied mathematics and Discrete mathematics. His Mathematical optimization research includes themes of Convergence, Sparse matrix and Convex optimization. His Combinatorics research is multidisciplinary, incorporating elements of Fixed point, Matrix norm, Factor graph, Rank and Statistics.

The concepts of his Estimator study are interwoven with issues in Optimization problem, Differential privacy and Covariance. He combines subjects such as Divide and conquer algorithms, Regularization and Principal component regression with his study of Applied mathematics. As a part of the same scientific family, Martin J. Wainwright mostly works in the field of Discrete mathematics, focusing on Linear programming and, on occasion, Linear code, Binary code, Posterior probability and Tree structure.

- Graphical Models, Exponential Families, and Variational Inference (2997 citations)
- Image denoising using scale mixtures of Gaussians in the wavelet domain (2020 citations)
- Network Coding for Distributed Storage Systems (1702 citations)

Martin J. Wainwright focuses on Algorithm, Mathematical optimization, Applied mathematics, Combinatorics and Minimax. His Algorithm study frequently links to related topics such as Artificial intelligence. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Convergence, Estimation theory, Graphical model, Markov chain and Convex optimization.

The Applied mathematics study which covers Regularization that intersects with Linear regression. His study in Combinatorics is interdisciplinary in nature, drawing from both Discrete mathematics, Matrix, Distribution, Function and Upper and lower bounds. His research in Discrete mathematics intersects with topics in Fixed point and Belief propagation.

- Algorithm (23.23%)
- Mathematical optimization (21.90%)
- Applied mathematics (19.47%)

- Algorithm (23.23%)
- Applied mathematics (19.47%)
- Combinatorics (17.48%)

Martin J. Wainwright mostly deals with Algorithm, Applied mathematics, Combinatorics, Minimax and False discovery rate. His work deals with themes such as Sampling, LogitBoost, Estimator and Graphical model, which intersect with Algorithm. His work is dedicated to discovering how Estimator, Pairwise comparison are connected with Set and Ranking and other disciplines.

His Applied mathematics research incorporates elements of Stochastic approximation, Matrix, Convex function, Regular polygon and Gradient descent. Martin J. Wainwright combines subjects such as Distribution, Order and Rank with his study of Combinatorics. His Minimax study results in a more complete grasp of Mathematical optimization.

- High-Dimensional Statistics: A Non-Asymptotic Viewpoint (291 citations)
- Statistical guarantees for the EM algorithm: From population to sample-based analysis (239 citations)
- Minimax Optimal Procedures for Locally Private Estimation (132 citations)

- Statistics
- Machine learning
- Algorithm

His primary areas of study are Algorithm, Applied mathematics, False discovery rate, Theoretical computer science and Combinatorics. His Algorithm research includes elements of Sampling, Discretization, Kernel, Metropolis–Hastings algorithm and Condition number. His work carried out in the field of Applied mathematics brings together such families of science as High-dimensional statistics, Convex function, Estimator, Optimization problem and Reinforcement learning.

His Combinatorics study combines topics in areas such as Distribution, Minimax and Markov chain Monte Carlo. The Complement study combines topics in areas such as Mathematical optimization and Convex optimization. Martin J. Wainwright incorporates Mathematical optimization and Estimation in his studies.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Graphical Models, Exponential Families, and Variational Inference

Martin J. Wainwright;Michael I. Jordan.

**(2008)**

3902 Citations

Image denoising using scale mixtures of Gaussians in the wavelet domain

J. Portilla;V. Strela;M.J. Wainwright;E.P. Simoncelli.

IEEE Transactions on Image Processing **(2003)**

2778 Citations

Network Coding for Distributed Storage Systems

A G Dimakis;P B Godfrey;Yunnan Wu;M J Wainwright.

IEEE Transactions on Information Theory **(2010)**

2041 Citations

Statistical Learning with Sparsity: The Lasso and Generalizations

Trevor Hastie;Robert Tibshirani;Martin Wainwright.

**(2015)**

1699 Citations

Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $ll _{1}$ -Constrained Quadratic Programming (Lasso)

M.J. Wainwright.

IEEE Transactions on Information Theory **(2009)**

1276 Citations

A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers

Sahand N. Negahban;Pradeep Ravikumar;Martin J. Wainwright;Bin Yu.

Statistical Science **(2012)**

1080 Citations

Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

J. C. Duchi;A. Agarwal;M. J. Wainwright.

IEEE Transactions on Automatic Control **(2012)**

926 Citations

High-dimensional Ising model selection using ℓ1-regularized logistic regression

Pradeep Ravikumar;Martin J. Wainwright;John D. Lafferty.

Annals of Statistics **(2010)**

900 Citations

MAP estimation via agreement on trees: message-passing and linear programming

M.J. Wainwright;T.S. Jaakkola;A.S. Willsky.

IEEE Transactions on Information Theory **(2005)**

758 Citations

High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence

Pradeep Ravikumar;Martin J. Wainwright;Garvesh Raskutti;Bin Yu.

Electronic Journal of Statistics **(2011)**

753 Citations

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Publications: 80

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