What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary scientific interests are in Artificial intelligence, Gaussian process, Machine learning, Algorithm and Mathematical optimization.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Expectation propagation and Pattern recognition.
His Gaussian process study integrates concerns from other disciplines, such as Covariance function, Inference, Gaussian function and Prior probability.
His Machine learning research integrates issues from Probabilistic logic and Bayesian inference.
His work deals with themes such as Hybrid Monte Carlo, Markov chain Monte Carlo and Regression, which intersect with Algorithm.
In Instance-based learning, Carl Edward Rasmussen works on issues like Online machine learning, which are connected to Computational learning theory.
His most cited work include:
- Gaussian Processes for Machine Learning (10390 citations)
- Gaussian processes in machine learning (1880 citations)
- A Unifying View of Sparse Approximate Gaussian Process Regression (1255 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Gaussian process, Artificial intelligence, Algorithm, Machine learning and Inference.
His Gaussian process study combines topics from a wide range of disciplines, such as Marginal likelihood, State space, Kriging, Mathematical optimization and Nonlinear system.
His Artificial intelligence research incorporates themes from Applied mathematics and Pattern recognition.
His study in the field of Artificial neural network, Instance-based learning and Active learning also crosses realms of Process.
Carl Edward Rasmussen works mostly in the field of Instance-based learning, limiting it down to topics relating to Online machine learning and, in certain cases, Computational learning theory.
His Inference research also works with subjects such as
- Markov chain Monte Carlo which is related to area like Prior probability,
- Hidden Markov model together with Gibbs sampling.
He most often published in these fields:
- Gaussian process (60.13%)
- Artificial intelligence (49.02%)
- Algorithm (29.41%)
What were the highlights of his more recent work (between 2017-2021)?
- Gaussian process (60.13%)
- Algorithm (29.41%)
- Inference (18.95%)
In recent papers he was focusing on the following fields of study:
Gaussian process, Algorithm, Inference, Hyperparameter and Upper and lower bounds are his primary areas of study.
The various areas that Carl Edward Rasmussen examines in his Gaussian process study include Marginal likelihood, Applied mathematics, Kernel and Kriging.
His work on Matching as part of his general Algorithm study is frequently connected to Variance, thereby bridging the divide between different branches of science.
His Inference study incorporates themes from Class, Dynamical systems theory, Bayesian linear regression and Statistical model.
His study looks at the intersection of Bayesian linear regression and topics like Benchmark with Machine learning.
He has researched Instability in several fields, including Artificial intelligence and Reinforcement learning.
Between 2017 and 2021, his most popular works were:
- Deep Convolutional Networks as shallow Gaussian Processes. (65 citations)
- Deep Convolutional Networks as shallow Gaussian Processes (46 citations)
- Rates of Convergence for Sparse Variational Gaussian Process Regression (39 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Gaussian process, Regression, Algorithm, Hyperparameter and Discrete mathematics.
His work in Gaussian process addresses subjects such as Inference, which are connected to disciplines such as Discrete time and continuous time and Posterior probability.
He combines subjects such as Bayesian optimization, Bayesian linear regression, Simple linear regression, Linear model and Benchmark with his study of Regression.
His study in the fields of Residual under the domain of Algorithm overlaps with other disciplines such as Limit.
Hyperparameter is the topic of his studies on Machine learning and Artificial intelligence.
Carl Edward Rasmussen interconnects Kullback–Leibler divergence, Process and Kriging in the investigation of issues within Discrete mathematics.
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