What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Mathematical optimization, Gaussian process, Artificial intelligence, Machine learning and Wireless sensor network.
His work on Submodular set function, Heuristics and Approximation algorithm as part of general Mathematical optimization research is frequently linked to Property, bridging the gap between disciplines.
His Submodular set function research is multidisciplinary, incorporating perspectives in Randomized algorithm, Greedy algorithm, Maximization and Cluster analysis.
His Approximation algorithm research includes elements of Digital media, Data mining and Information cascade.
The various areas that he examines in his Artificial intelligence study include Identification and Pattern recognition.
As part of one scientific family, he deals mainly with the area of Mutual information, narrowing it down to issues related to the Entropy, and often Global Positioning System and Empirical research.
His most cited work include:
- Cost-effective outbreak detection in networks (1703 citations)
- Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies (1192 citations)
- Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (877 citations)
What are the main themes of his work throughout his whole career to date?
Andreas Krause mainly focuses on Mathematical optimization, Artificial intelligence, Submodular set function, Machine learning and Gaussian process.
His Mathematical optimization research is multidisciplinary, incorporating elements of Sampling and Regret.
Many of his research projects under Artificial intelligence are closely connected to Set with Set, tying the diverse disciplines of science together.
His studies in Submodular set function integrate themes in fields like Maximization, Set function, Inference, Probabilistic logic and Automatic summarization.
His Machine learning study integrates concerns from other disciplines, such as Crowdsourcing, Data mining and Bayesian probability.
Andreas Krause connects Gaussian process with Dynamical system in his research.
He most often published in these fields:
- Mathematical optimization (34.11%)
- Artificial intelligence (27.34%)
- Submodular set function (21.50%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (34.11%)
- Artificial intelligence (27.34%)
- Machine learning (21.26%)
In recent papers he was focusing on the following fields of study:
Andreas Krause mostly deals with Mathematical optimization, Artificial intelligence, Machine learning, Gaussian process and Algorithm.
His Mathematical optimization study combines topics in areas such as Sampling and Regret.
In general Artificial intelligence, his work in Reinforcement learning, Artificial neural network and Classifier is often linked to Structure linking many areas of study.
The Active learning and Leverage research Andreas Krause does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Function, Space and Set, therefore creating a link between diverse domains of science.
While the research belongs to areas of Algorithm, Andreas Krause spends his time largely on the problem of Inference, intersecting his research to questions surrounding Probabilistic logic.
His Submodular set function research integrates issues from Feature selection and Automatic summarization.
Between 2017 and 2021, his most popular works were:
- A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions (138 citations)
- Learning-Based Model Predictive Control for Safe Exploration (128 citations)
- Fake News Detection in Social Networks via Crowd Signals (105 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Mathematical optimization, Artificial intelligence, Gaussian process, Machine learning and Bayesian optimization.
Andreas Krause combines subjects such as Sampling and Regret with his study of Mathematical optimization.
In his study, Thompson sampling, Frequentist inference and Sampling distribution is inextricably linked to Reproducing kernel Hilbert space, which falls within the broad field of Regret.
Andreas Krause interconnects Constraint and Inverted pendulum in the investigation of issues within Artificial intelligence.
In general Machine learning study, his work on Active learning and Reinforcement learning often relates to the realm of Space, Function and Set, thereby connecting several areas of interest.
His Bayesian optimization study incorporates themes from Temperature control and Optimization problem.
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