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.
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.
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.
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.
Holographic embeddings of knowledge graphs
Maximilian Nickel;Lorenzo Rosasco;Tomaso Poggio.
national conference on artificial intelligence (2016)
On Early Stopping in Gradient Descent Learning
Yuan Yao;Lorenzo Rosasco;Lorenzo Rosasco;Andrea Caponnetto;Andrea Caponnetto.
Constructive Approximation (2007)
Kernels for Vector-Valued Functions: A Review
Mauricio A. Álvarez;Lorenzo Rosasco;Neil D. Lawrence.
Are loss functions all the same
Lorenzo Rosasco;Ernesto De Vito;Andrea Caponnetto;Michele Piana.
Neural Computation (2004)
Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review
Tomaso A. Poggio;Hrushikesh Mhaskar;Hrushikesh Mhaskar;Lorenzo Rosasco;Brando Miranda.
International Journal of Automation and Computing (2017)
Elastic-net regularization in learning theory
Christine De Mol;Ernesto De Vito;Lorenzo Rosasco.
Journal of Complexity (2009)
On regularization algorithms in learning theory
Frank Bauer;Sergei Pereverzev;Lorenzo Rosasco.
Journal of Complexity (2007)
Less is more: Nyström computational regularization
Alessandro Rudi;Raffaello Camoriano;Lorenzo Rosasco.
neural information processing systems (2015)
Learning from Examples as an Inverse Problem
Ernesto De Vito;Lorenzo Rosasco;Andrea Caponnetto;Umberto De Giovannini.
Journal of Machine Learning Research (2005)
Generalization Properties of Learning with Random Features
Alessandro Rudi;Lorenzo Rosasco.
neural information processing systems (2017)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: