2020 - ACM Fellow For contributions to automated algorithm selection and configuration for optimization and machine learning
2015 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the field of automated reasoning and the development of widely used methods for algorithm selection and configuration
Holger H. Hoos spends much of his time researching Local search, Algorithm, Mathematical optimization, Machine learning and Artificial intelligence. His work carried out in the field of Local search brings together such families of science as Heuristics, Heuristic, Vertex, Solver and Boolean satisfiability problem. As a part of the same scientific family, Holger H. Hoos mostly works in the field of Algorithm, focusing on Categorical variable and, on occasion, Computational problem.
Travelling salesman problem and Optimization problem are the primary areas of interest in his Mathematical optimization study. His Machine learning research integrates issues from Independence and Conditional dependence. The concepts of his Artificial intelligence study are interwoven with issues in Mathematical economics, Ceteris paribus and Preference.
His primary areas of study are Algorithm, Local search, Mathematical optimization, Artificial intelligence and Solver. His Algorithm research includes elements of Range, Function and Set. He focuses mostly in the field of Local search, narrowing it down to matters related to Search algorithm and, in some cases, Stochastic programming.
His work in the fields of Travelling salesman problem, Iterated local search, Heuristic and Ant colony optimization algorithms overlaps with other areas such as Portfolio. His research in Artificial intelligence focuses on subjects like Machine learning, which are connected to Data mining. His Solver research focuses on Theoretical computer science and how it relates to Scheduling.
Holger H. Hoos focuses on Algorithm, Solver, Mathematical optimization, Set and Range. His is doing research in State, Local search and Travelling salesman problem, both of which are found in Algorithm. His Solver research incorporates themes from Time complexity, Algorithm design, Theoretical computer science and Parameterized complexity.
His research brings together the fields of Algorithm configuration and Mathematical optimization. While the research belongs to areas of Set, he spends his time largely on the problem of Benchmark, intersecting his research to questions surrounding Hyperparameter and Population-based incremental learning. His Range study combines topics from a wide range of disciplines, such as Quality, Variety, Machine learning and Hyperparameter optimization.
His scientific interests lie mostly in Mathematical optimization, Algorithm, Solver, Machine learning and Artificial intelligence. The study incorporates disciplines such as Algorithm configuration and Set in addition to Mathematical optimization. His Algorithm study incorporates themes from Lasso and Markov chain.
His Solver research incorporates elements of Theoretical computer science, Boolean satisfiability problem, Benchmark and Answer set programming. His research in Machine learning intersects with topics in Recall, Data mining and Interface. His study in the field of Codebook is also linked to topics like Running time.
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.
MAX-MIN Ant system
Thomas Stützle;Holger H. Hoos.
Future Generation Computer Systems (2000)
Stochastic Local Search: Foundations & Applications
Holger Hoos;Thomas Sttzle.
Sequential model-based optimization for general algorithm configuration
Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown.
learning and intelligent optimization (2011)
MAX-MIN Ant System and local search for the traveling salesman problem
T. Stutzle;H. Hoos.
ieee international conference on evolutionary computation (1997)
CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements
Craig Boutilier;Ronen I. Brafman;Carmel Domshlak;Holger H. Hoos.
Journal of Artificial Intelligence Research (2004)
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Chris Thornton;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown.
knowledge discovery and data mining (2013)
SATzilla: portfolio-based algorithm selection for SAT
Lin Xu;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown.
Journal of Artificial Intelligence Research (2008)
ParamILS: An Automatic Algorithm Configuration Framework
Frank Hutter;Thomas Stuetzle;Kevin Leyton-Brown;Holger H. Hoos.
arXiv e-prints (2014)
Critical assessment of automated flow cytometry data analysis techniques
Nima Aghaeepour;Greg Finak.
Nature Methods (2013)
Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA
Lars Kotthoff;Chris Thornton;Holger H. Hoos;Frank Hutter.
Journal of Machine Learning Research (2017)
Profile was last updated on December 6th, 2021.
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