What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
His main research concerns Algorithm, Artificial intelligence, Machine learning, Combinatorial auction and Mathematical optimization.
His study in the field of Local search, Algorithm configuration and Algorithm design also crosses realms of Constraint satisfaction and Variance.
His research in Artificial intelligence intersects with topics in Repeated game and Game theory.
His Cooperative game theory and Algorithmic game theory study in the realm of Game theory interacts with subjects such as Markov decision process.
Kevin Leyton-Brown interconnects Theoretical computer science and Data mining in the investigation of issues within Machine learning.
In his research on the topic of Mathematical optimization, Component, Tree and Categorical variable is strongly related with Boolean satisfiability problem.
His most cited work include:
- Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations (1331 citations)
- Sequential model-based optimization for general algorithm configuration (1111 citations)
- Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms (697 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Mathematical optimization, Algorithm, Machine learning and Theoretical computer science.
His Artificial intelligence research incorporates elements of Generalization and Game theory.
His Game theory research is multidisciplinary, incorporating elements of Stackelberg competition, Multi-agent system and Mechanism design.
His Mathematical optimization research incorporates themes from Mathematical economics and Combinatorial auction.
In general Algorithm study, his work on Satisfiability, Local search and Integer programming often relates to the realm of Portfolio, thereby connecting several areas of interest.
His Machine learning study incorporates themes from Range, Variety and Data mining.
He most often published in these fields:
- Artificial intelligence (22.38%)
- Mathematical optimization (21.43%)
- Algorithm (20.00%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (21.43%)
- Artificial intelligence (22.38%)
- Incentive (6.19%)
In recent papers he was focusing on the following fields of study:
Kevin Leyton-Brown mainly focuses on Mathematical optimization, Artificial intelligence, Incentive, Machine learning and Reverse auction.
His Mathematical optimization research focuses on Algorithm configuration and how it relates to Procrastination.
His research integrates issues of Active learning and Natural language processing in his study of Artificial intelligence.
His work on Model selection and Artificial neural network as part of general Machine learning research is frequently linked to Hyperparameter optimization and Outcome, bridging the gap between disciplines.
Kevin Leyton-Brown has included themes like Bayesian optimization, Data mining, Feature selection and Hyperparameter in his Model selection study.
His Nash equilibrium study combines topics from a wide range of disciplines, such as Common value auction and Game theory.
Between 2016 and 2021, his most popular works were:
- Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA (312 citations)
- Deep IV: a flexible approach for counterfactual prediction (55 citations)
- Economics and computer science of a radio spectrum reallocation. (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Kevin Leyton-Brown focuses on Artificial intelligence, Supervised learning, Mathematical optimization, Empirical algorithmics and Machine learning.
His study of Deep learning is a part of Artificial intelligence.
In his study, Solver is strongly linked to Theoretical computer science, which falls under the umbrella field of Deep learning.
His work deals with themes such as Procrastination, Algorithm configuration, Adaptive algorithm and Parameterized complexity, which intersect with Mathematical optimization.
The Empirical algorithmics study combines topics in areas such as Algorithm and Boolean satisfiability problem.
His study in the fields of Model selection and Artificial neural network under the domain of Machine learning overlaps with other disciplines such as Function and Outcome.
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.
