What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
Mark Schmidt mainly investigates Mathematical optimization, Convex optimization, Rate of convergence, Artificial intelligence and Applied mathematics.
His Mathematical optimization research includes elements of Random coordinate descent and Projection.
His biological study spans a wide range of topics, including Parallel algorithm and Computation.
His Rate of convergence research integrates issues from Convex function and Support vector machine.
His studies deal with areas such as Machine learning, Search algorithm, Computer vision and Pattern recognition as well as Artificial intelligence.
His Applied mathematics study combines topics in areas such as Gradient descent, Stochastic gradient descent and Stochastic optimization.
His most cited work include:
- Minimizing finite sums with the stochastic average gradient (518 citations)
- A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets (421 citations)
- Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition (384 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Artificial intelligence, Rate of convergence, Applied mathematics and Algorithm.
Within one scientific family, Mark Schmidt focuses on topics pertaining to Convex optimization under Mathematical optimization, and may sometimes address concerns connected to Subgradient method and Regularization.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
He has included themes like Stochastic gradient descent, Selection, Interpolation, Gradient descent and Gradient method in his Rate of convergence study.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Convex function and Constant.
His studies in Algorithm integrate themes in fields like Graphical model and Inference.
He most often published in these fields:
- Mathematical optimization (29.27%)
- Artificial intelligence (28.46%)
- Rate of convergence (25.20%)
What were the highlights of his more recent work (between 2019-2021)?
- Applied mathematics (24.39%)
- Artificial intelligence (28.46%)
- Convex function (8.94%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Applied mathematics, Artificial intelligence, Convex function, Inference and Algorithm.
His Applied mathematics study combines topics from a wide range of disciplines, such as Gradient descent and Kullback–Leibler divergence.
He combines subjects such as Machine learning and Stochastic optimization with his study of Artificial intelligence.
His Convex function study incorporates themes from Line search, Interpolation, Kernel and Convex optimization.
His work carried out in the field of Interpolation brings together such families of science as Binary classification, Rate of convergence, Broyden–Fletcher–Goldfarb–Shanno algorithm and Hessian matrix.
His study on Convex optimization also encompasses disciplines like
- Subgradient method, which have a strong connection to Sublinear function,
- Lipschitz continuity that intertwine with fields like Thompson sampling, Heuristics, Mathematical optimization and Global optimization.
Between 2019 and 2021, his most popular works were:
- Variance-Reduced Methods for Machine Learning (10 citations)
- Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search) (7 citations)
- Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. (6 citations)
In his most recent research, the most cited papers focused on:
- Machine learning
- Artificial intelligence
- Mathematical optimization
Mark Schmidt spends much of his time researching Artificial intelligence, Applied mathematics, Interpolation, Constant and Data modeling.
His study in Pascal, Embedding, Pixel, Image segmentation and Segmentation is carried out as part of his Artificial intelligence studies.
His Applied mathematics research incorporates themes from Binary classification, Rate of convergence and Broyden–Fletcher–Goldfarb–Shanno algorithm.
His Interpolation research includes themes of Line search, Convex function, Hessian matrix and Convex optimization.
His study in Constant is interdisciplinary in nature, drawing from both Mathematical optimization, Heuristics, Thompson sampling and Lipschitz continuity.
His Data modeling study spans across into areas like Key, Stochastic gradient descent, Stochastic optimization, Variance reduction and Machine learning.
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