Özgür Şimşek

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Andrew BartoUniversity of Massachusetts AmherstCorreu electrònic verificat a cs.umass.edu
Marcus BuckmannResearcher, Bank of EnglandCorreu electrònic verificat a bankofengland.co.uk
David JensenProfessor of Computer Science, University of Massachusetts AmherstCorreu electrònic verificat a cs.umass.edu
Konstantinos V. KatsikopoulosProf. of Behavioral Science & Deputy Head (Dean) for Research, Uni. of Southampton Business SchoolCorreu electrònic verificat a soton.ac.uk
Gerd GigerenzerMax Planck Institute for Human DevelopmentCorreu electrònic verificat a mpib-berlin.mpg.de
Alicia WolfePostdoc, Wesleyan UniversityCorreu electrònic verificat a wesleyan.edu
Jennifer NevilleMicrosoft Research, Purdue UniversityCorreu electrònic verificat a microsoft.com
Jan Malte LichtenbergAmazon, University of BathCorreu electrònic verificat a amazon.com
John L. Campbell, Ph.D., CHFP, PMPExponent, Inc.Correu electrònic verificat a exponent.com
Kristina BluwsteinResearch Economist, Bank of EnglandCorreu electrònic verificat a bankofengland.co.uk
Andreas JosephResearch Economist, Bank of EnglandCorreu electrònic verificat a bankofengland.co.uk
Sujit KapadiaBank of EnglandCorreu electrònic verificat a bankofengland.co.uk
Amit KothiyalMax Planck Institute for Human DevelopmentCorreu electrònic verificat a mpib-berlin.mpg.de
Daniel BeecheyPhD student, University of BathCorreu electrònic verificat a bath.ac.uk
Ian DurbachUniversity of St Andrews, University of Cape TownCorreu electrònic verificat a st-andrews.ac.uk
Jack SaundersResearch Associate, University of Western AustraliaCorreu electrònic verificat a uwa.edu.au
Joshua B. EvansUniversity of BathCorreu electrònic verificat a bath.ac.uk

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Özgür Şimşek

Professor of Artificial Intelligence, University of Bath

Correu electrònic verificat a bath.ac.uk - Pàgina d'inici

Machine learning artificial intelligence network science bounded rationality


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
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	336	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	290	2004
Skill characterization based on betweenness Ö Şimşek, A Barto Advances in neural information processing systems 21, 2008	203	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	165	2005
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
An intrinsic reward mechanism for efficient exploration Ö Şimşek, AG Barto Proceedings of the 23rd international conference on Machine learning, 833-840, 2006	142	2006
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	98	2023
Classification in the wild: The science and art of transparent decision making KV Katsikopoulos, Ö Şimşek, M Buckmann, G Gigerenzer MIT Press, 2021	76*	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	69	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	44	2017
Secondary-task measures of driver workload BH Kantowitz, O Simsek Stress, workload, and fatigue, 395-408, 2000	43	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
Behavioral building blocks for autonomous agents: description, identification, and learning O Simsek University of Massachusetts Amherst, 2008	35	2008
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	34	2022
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	33	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	31	2004
Computational techniques used in the driver performance model of the interactive highway safety design model WH Levison, O Simsek, AC Bittner Jr, SJ Hunn Transportation Research Record 1779 (1), 17-25, 2001	19	2001

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