Özgür Şimşek

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Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Marcus BuckmannResearcher, Bank of EnglandVerified email at bankofengland.co.uk
David JensenProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Konstantinos V. KatsikopoulosProf. of Behavioral Science & Deputy Head (Dean) for Research, Uni. of Southampton Business SchoolVerified email at soton.ac.uk
Gerd GigerenzerMax Planck Institute for Human DevelopmentVerified email at mpib-berlin.mpg.de
Alicia WolfePostdoc, Wesleyan UniversityVerified email at wesleyan.edu
Jennifer NevilleMicrosoft Research, Purdue UniversityVerified email at microsoft.com
Jan Malte LichtenbergAmazon, University of BathVerified email at amazon.com
John L. Campbell, Ph.D., CHFP, PMPExponent, Inc.Verified email at exponent.com
Kristina BluwsteinResearch Economist, Bank of EnglandVerified email at bankofengland.co.uk
Andreas JosephResearch Economist, Bank of EnglandVerified email at bankofengland.co.uk
Sujit KapadiaBank of EnglandVerified email at bankofengland.co.uk
Amit KothiyalMax Planck Institute for Human DevelopmentVerified email at mpib-berlin.mpg.de
Daniel BeecheyPhD student, University of BathVerified email at bath.ac.uk
Ian DurbachUniversity of St Andrews, University of Cape TownVerified email at st-andrews.ac.uk
Joshua B. EvansUniversity of BathVerified email at bath.ac.uk
Jack SaundersResearch Fellow, University of BristolVerified email at bristol.ac.uk

Özgür Şimşek

Professor of Artificial Intelligence, University of Bath

Verified email at bath.ac.uk - Homepage

Machine learning artificial intelligence network science bounded rationality


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Identifying useful subgoals in reinforcement learning by local graph partitioning Ö Şimşek, AP Wolfe, AG Barto Proceedings of the 22nd international conference on Machine learning, 816-823, 2005	339	2005
Using relative novelty to identify useful temporal abstractions in reinforcement learning Ö Şimşek, AG Barto Proceedings of the twenty-first international conference on Machine learning, 95, 2004	292	2004
Skill characterization based on betweenness Ö Şimşek, A Barto Advances in neural information processing systems 21, 2008	207	2008
Using relational knowledge discovery to prevent securities fraud J Neville, Ö Şimşek, D Jensen, J Komoroske, K Palmer, H Goldberg Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005	162	2005
An intrinsic reward mechanism for efficient exploration Ö Şimşek, AG Barto Proceedings of the 23rd international conference on Machine learning, 833-840, 2006	146	2006
Navigating networks by using homophily and degree Ö Şimşek, D Jensen Proceedings of the National Academy of Sciences 105 (35), 12758-12762, 2008	143	2008
Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach K Bluwstein, M Buckmann, A Joseph, S Kapadia, Ö Şimşek Journal of International Economics 145, 103773, 2023	108	2023
Classification in the wild: The science and art of transparent decision making KV Katsikopoulos, Ö Şimşek, M Buckmann, G Gigerenzer MIT Press, 2021	82*	2021
Linear decision rule as aspiration for simple decision heuristics Ö Şimşek Advances in neural information processing systems 26, 2013	70	2013
Decentralized search in networks using homophily and degree disparity O Simsek, D Jensen IJCAI, 304-310, 2005	69	2005
Intrinsic motivation for reinforcement learning systems AG Barto, O Simsek Proceedings of the thirteenth yale workshop on adaptive and learning systems …, 2005	68	2005
On-road versus simulator data in driver model development driver performance model experience AC Bittner Jr, O Simsek, WH Levison, JL Campbell Transportation research record 1803 (1), 38-44, 2002	53	2002
Simple regression models JM Lichtenberg, Ö Şimşek Imperfect decision makers: Admitting real-world rationality, 13-25, 2017	45	2017
Secondary-task measures of driver workload BH Kantowitz, O Simsek Stress, workload, and fatigue, 395-408, 2000	42	2000
Learning from small samples: An analysis of simple decision heuristics Ö Şimşek, M Buckmann Advances in neural information processing systems 28, 2015	40	2015
Transparent modeling of influenza incidence: Big data or a single data point from psychological theory? KV Katsikopoulos, Ö Şimşek, M Buckmann, G Gigerenzer International Journal of Forecasting 38 (2), 613-619, 2022	38	2022
Behavioral building blocks for autonomous agents: description, identification, and learning O Simsek University of Massachusetts Amherst, 2008	35	2008
Why most decisions are easy in tetris—and perhaps in other sequential decision problems, as well Ö Şimşek, S Algorta, A Kothiyal International Conference on Machine Learning, 1757-1765, 2016	34	2016
Autocorrelation and relational learning: Challenges and opportunities J Neville, Ö Şimşek, D Jensen ICML 2004 Workshop on Statistical Relational Learning and its Connections to …, 2004	32	2004
The game of Tetris in machine learning S Algorta, Ö Şimşek arXiv preprint arXiv:1905.01652, 2019	19	2019

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