What is he best known for?
The fields of study he is best known for:
- Algorithm
- Statistics
- Mathematical optimization
His primary scientific interests are in Algorithm, Convex function, Coordinate descent, Mathematical optimization and Sampling.
His Algorithm study combines topics in areas such as Separable space and Stochastic optimization.
His Convex function research incorporates elements of Combinatorics, Gradient descent, Lasso, Convex optimization and Function.
His Coordinate descent study integrates concerns from other disciplines, such as Linear system, Computational intelligence and Type.
His Mathematical optimization research incorporates themes from Robustification, Local algorithm, Rate of convergence, Speedup and Random coordinate descent.
His study in the fields of Importance sampling under the domain of Sampling overlaps with other disciplines such as Context.
His most cited work include:
- Federated Learning: Strategies for Improving Communication Efficiency (1080 citations)
- Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function (579 citations)
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence (466 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Algorithm, Convex function, Applied mathematics, Mathematical optimization and Coordinate descent.
He has included themes like Matrix, Simple and Compression in his Algorithm study.
His biological study spans a wide range of topics, including Combinatorics, Minification, Convex optimization, Function and Differentiable function.
His Applied mathematics research is multidisciplinary, incorporating elements of Rate of convergence, Stochastic gradient descent, Hessian matrix and Importance sampling.
His work deals with themes such as Convergence, Dual and Speedup, which intersect with Mathematical optimization.
His studies deal with areas such as Discrete mathematics, Acceleration, Optimization problem, Random coordinate descent and Lipschitz continuity as well as Coordinate descent.
He most often published in these fields:
- Algorithm (37.69%)
- Convex function (36.94%)
- Applied mathematics (30.60%)
What were the highlights of his more recent work (between 2019-2021)?
- Convex function (36.94%)
- Applied mathematics (30.60%)
- Algorithm (37.69%)
In recent papers he was focusing on the following fields of study:
Peter Richtárik mainly focuses on Convex function, Applied mathematics, Algorithm, Rate of convergence and Variance reduction.
His Convex function research is multidisciplinary, incorporating perspectives in Function, Differentiable function, Importance sampling and Minification.
The various areas that he examines in his Applied mathematics study include Iterative method, Stochastic gradient descent, Newton's method and Coordinate descent.
His work in the fields of Computation overlaps with other areas such as Dither.
His Rate of convergence study combines topics from a wide range of disciplines, such as Quadratic equation, Gradient descent, Ergodic theory, Mathematical optimization and Convex optimization.
His Mathematical optimization research includes themes of Sampling, Convergence and Fixed point.
Between 2019 and 2021, his most popular works were:
- Tighter Theory for Local SGD on Identical and Heterogeneous Data (57 citations)
- Federated Learning of a Mixture of Global and Local Models (41 citations)
- Natural Compression for Distributed Deep Learning (34 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Mathematical analysis
His main research concerns Applied mathematics, Stochastic gradient descent, Rate of convergence, Variance reduction and Convex function.
His work is dedicated to discovering how Applied mathematics, Function are connected with Constant, Quadratic equation and Sublinear function and other disciplines.
His work in Stochastic gradient descent tackles topics such as Stochastic optimization which are related to areas like Hessian matrix, Numerical linear algebra, Jacobian matrix and determinant and Importance sampling.
His research in Rate of convergence intersects with topics in Gradient descent, Iterated function, Mathematical optimization and Compression.
His work in Gradient descent addresses issues such as Combinatorics, which are connected to fields such as Federated learning.
His research combines Computation and Convex function.
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