What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Gianluigi Pillonetto mainly investigates Mathematical optimization, Algorithm, System identification, Convex optimization and Interior point method.
The various areas that Gianluigi Pillonetto examines in his Mathematical optimization study include Bayes estimator, Estimator, Inverse problem, Applied mathematics and Function.
In his study, Gianluigi Pillonetto carries out multidisciplinary Algorithm and Gaussian process research.
Gianluigi Pillonetto has included themes like Marginal likelihood, Linear system and Kernel method in his System identification study.
His biological study spans a wide range of topics, including Convex function and Newton's method.
In his work, Maximum a posteriori estimation, Robust statistics, Smoothing and Sequence is strongly intertwined with Kalman filter, which is a subfield of Interior point method.
His most cited work include:
- Survey Kernel methods in system identification, machine learning and function estimation: A survey (370 citations)
- A new kernel-based approach for linear system identification (284 citations)
- Prediction error identification of linear systems: A nonparametric Gaussian regression approach (160 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Algorithm, Applied mathematics, Estimator and Gaussian process.
His Mathematical optimization research incorporates elements of Marginal likelihood, Kernel, Spline, Impulse response and System identification.
In his research, Computational complexity theory is intimately related to Machine learning, which falls under the overarching field of System identification.
The concepts of his Algorithm study are interwoven with issues in Kalman filter, Linear system, Outlier and Variable kernel density estimation.
His biological study deals with issues like Interior point method, which deal with fields such as Piecewise linear function and Convex optimization.
In his research on the topic of Estimator, Multiple kernel learning is strongly related with Lasso.
He most often published in these fields:
- Mathematical optimization (45.25%)
- Algorithm (38.01%)
- Applied mathematics (23.53%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (38.01%)
- Kernel (16.74%)
- System identification (20.81%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Algorithm, Kernel, System identification, Gaussian process and Applied mathematics.
His study of Regularization is a part of Algorithm.
His Kernel study integrates concerns from other disciplines, such as Robot, Mathematical optimization, Kriging and Identification.
His research in Mathematical optimization intersects with topics in Stochastic simulation, Hybrid system and Nonlinear system.
His System identification research is multidisciplinary, incorporating perspectives in Exponential stability, Reproducing kernel Hilbert space, Hilbert space and Impulse response.
His Applied mathematics research focuses on Linear system identification and how it relates to Cross-validation.
Between 2017 and 2021, his most popular works were:
- System Identification: A Machine Learning Perspective (16 citations)
- The quest for the right kernel in Bayesian impulse response identification: The use of OBFs (13 citations)
- Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His main research concerns Algorithm, System identification, Reproducing kernel Hilbert space, Gaussian process and Kalman filter.
His Algorithm research includes elements of Monomial, Probabilistic logic, Kernel and Marginal likelihood.
His Kernel research incorporates themes from Mathematical optimization and Extended Kalman filter, Fast Kalman filter.
His System identification research includes themes of Perspective, Machine learning, Kernel embedding of distributions and Kernel principal component analysis.
His work carried out in the field of Reproducing kernel Hilbert space brings together such families of science as Parametric statistics and Impulse response.
His Kalman filter study also includes fields such as
- Covariance that connect with fields like Smoothing, Convex optimization and Interior point method,
- Cross-validation which connect with Linear system identification.
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