What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Mathematical optimization, Algorithm, Kernel method, Applied mathematics and Artificial intelligence.
His Mathematical optimization research is multidisciplinary, incorporating elements of Feature selection, Reproducing kernel Hilbert space, Hilbert space and Regularization perspectives on support vector machines.
His study in Algorithm is interdisciplinary in nature, drawing from both Convex optimization, Proximal Gradient Methods and Nonparametric statistics.
Lorenzo Rosasco interconnects Landweber iteration, Early stopping, Statistical learning theory, Gradient descent and Square in the investigation of issues within Applied mathematics.
Lorenzo Rosasco has included themes like Backpropagation, Stochastic gradient descent and Gradient method in his Gradient descent study.
The concepts of his Artificial intelligence study are interwoven with issues in Theoretical computer science, Visual cortex and Pattern recognition.
His most cited work include:
- Holographic embeddings of knowledge graphs (550 citations)
- On Early Stopping in Gradient Descent Learning (463 citations)
- On Early Stopping in Gradient Descent Learning (463 citations)
What are the main themes of his work throughout his whole career to date?
Lorenzo Rosasco focuses on Artificial intelligence, Regularization, Applied mathematics, Algorithm and Mathematical optimization.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence.
His Regularization study combines topics from a wide range of disciplines, such as Feature selection, Early stopping, Inverse problem and Regular polygon.
His biological study spans a wide range of topics, including Stochastic gradient descent, Diagonal, Stability, Hilbert space and Gradient descent.
His studies in Algorithm integrate themes in fields like Nonparametric statistics, Reproducing kernel Hilbert space, Convergence of random variables, Kernel method and Function.
In his study, Polynomial kernel and Kernel principal component analysis is inextricably linked to Kernel embedding of distributions, which falls within the broad field of Mathematical optimization.
He most often published in these fields:
- Artificial intelligence (30.85%)
- Regularization (33.90%)
- Applied mathematics (30.85%)
What were the highlights of his more recent work (between 2019-2021)?
- Applied mathematics (30.85%)
- Regularization (33.90%)
- Artificial intelligence (30.85%)
In recent papers he was focusing on the following fields of study:
His main research concerns Applied mathematics, Regularization, Artificial intelligence, Stability and Kernel.
He combines subjects such as Dimension, Random matrix and Least squares with his study of Applied mathematics.
His research in Regularization intersects with topics in Convex function, Regular polygon, Linear model and Inverse problem.
His studies examine the connections between Regular polygon and genetics, as well as such issues in Iterated function, with regards to Convergence of random variables, Function and Algorithm.
His work carried out in the field of Stability brings together such families of science as Early stopping, Diagonal, Interpolation, Divergence and Condition number.
In his work, Riemannian manifold, Reproducing kernel Hilbert space and Hilbert space is strongly intertwined with Sobolev space, which is a subfield of Kernel.
Between 2019 and 2021, his most popular works were:
- Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces (25 citations)
- Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces (25 citations)
- Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Lorenzo Rosasco mostly deals with Function, Algorithm, Convergence of random variables, Space and Square.
His Function research is multidisciplinary, incorporating perspectives in Iterated function, Relaxation, Regular polygon and Extension.
His work deals with themes such as Structured prediction, Structure, Theoretical computer science and Probability measure, which intersect with Space.
His research investigates the connection with Structure and areas like Manifold which intersect with concerns in Computation and Estimator.
His Estimator research is multidisciplinary, relying on both Stability and Applied mathematics.
His studies deal with areas such as Principal component regression and Reproducing kernel Hilbert space, Hilbert space as well as Square.
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.
