What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Data mining, Minimum description length, Artificial intelligence, Theoretical computer science and Algorithm are his primary areas of study.
His Data mining research includes themes of Bayesian information criterion, Principle of maximum entropy, Heuristic, Transaction data and Probabilistic logic.
His work deals with themes such as Key and Benchmark data, which intersect with Bayesian information criterion.
His Artificial intelligence research integrates issues from Relational database, Categorical variable and Pattern recognition.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Construct, Graph theory, Spotting and Graph.
His Algorithm study often links to related topics such as Classifier.
His most cited work include:
- Krimp: mining itemsets that compress (236 citations)
- Spotting Culprits in Epidemics: How Many and Which Ones? (168 citations)
- Item Sets that Compress. (147 citations)
What are the main themes of his work throughout his whole career to date?
Jilles Vreeken focuses on Data mining, Artificial intelligence, Theoretical computer science, Minimum description length and Algorithm.
Jilles Vreeken combines subjects such as Principle of maximum entropy, Key and Multivariate statistics with his study of Data mining.
His research investigates the connection between Artificial intelligence and topics such as Machine learning that intersect with problems in Estimator.
Jilles Vreeken has researched Theoretical computer science in several fields, including Visualization, Constraint and Graph.
Jilles Vreeken usually deals with Minimum description length and limits it to topics linked to Kolmogorov complexity and Causal inference and Margin.
His Algorithm study integrates concerns from other disciplines, such as Random variable and Joint probability distribution.
He most often published in these fields:
- Data mining (36.04%)
- Artificial intelligence (22.84%)
- Theoretical computer science (19.29%)
What were the highlights of his more recent work (between 2019-2021)?
- Theoretical computer science (19.29%)
- Machine learning (13.20%)
- Artificial intelligence (22.84%)
In recent papers he was focusing on the following fields of study:
Jilles Vreeken spends much of his time researching Theoretical computer science, Machine learning, Artificial intelligence, Estimator and Algorithm.
Jilles Vreeken has included themes like Minimum description length, Spurious relationship, Contrast and Graph embedding in his Theoretical computer science study.
Jilles Vreeken works mostly in the field of Minimum description length, limiting it down to topics relating to Kolmogorov complexity and, in certain cases, Margin, as a part of the same area of interest.
His study in the field of Knowledge graph also crosses realms of Improved performance, Model test and Class.
His Algorithm research incorporates themes from Principle of maximum entropy, Event, Reliability and Heuristic.
His Functional dependency study is related to the wider topic of Data mining.
Between 2019 and 2021, his most popular works were:
- Identifying domains of applicability of machine learning models for materials science. (12 citations)
- Towards Plausible Graph Anonymization (7 citations)
- What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization (5 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Machine learning, Artificial intelligence, Principle of maximum entropy, Algorithm and Theoretical computer science.
His work on Errors-in-variables models as part of general Machine learning research is frequently linked to Model test, Improved performance and Class, thereby connecting diverse disciplines of science.
His work on Question answering as part of general Artificial intelligence research is frequently linked to Context, bridging the gap between disciplines.
As a member of one scientific family, Jilles Vreeken mostly works in the field of Principle of maximum entropy, focusing on Data modeling and, on occasion, Joint probability distribution.
The various areas that Jilles Vreeken examines in his Algorithm study include Entropy, Maximum entropy probability distribution and Relaxation.
His studies deal with areas such as Mixture model and Graph as well as Theoretical computer science.
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