What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Algorithm (23.23%)
- Mathematical optimization (21.90%)
- Applied mathematics (19.47%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (23.23%)
- Applied mathematics (19.47%)
- Combinatorics (17.48%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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