Gergely Neu

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Gabor LugosiICREA and Universitat Pompeu FabraVerified email at upf.edu
Csaba SzepesvariDeepMind & University of AlbertaVerified email at cs.ualberta.ca
Andras GyorgyDeepMindVerified email at ualberta.ca
Anders JonssonArtificial Intelligence and Machine Learning group, Universitat Pompeu FabraVerified email at upf.edu
Michal ValkoDeepMind Paris & Inria France & ENS MVAVerified email at deepmind.com
Tomáš KocákENS de LyonVerified email at ens-lyon.fr
Cristina CanoAssociate Professor, WINE Group, Universitat Oberta de CatalunyaVerified email at uoc.edu
Vicenç GómezArtificial Intelligence and Machine Learning research group, Universitat Pompeu FabraVerified email at upf.edu
Sergio Barrachina-MuñozResearcher in CTTCVerified email at cttc.cat
Julia OlkhovskayaUniversitat Pompeu FabraVerified email at upf.edu
Gábor BartókETH ZurichVerified email at ualberta.ca
András AntosBudapest University of Technology and EconomicsVerified email at cs.bme.hu
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it
Luc DevroyeMcGill UniversityVerified email at cs.mcgill.ca
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di MilanoVerified email at unimi.it
Claudio GentileGoogle Research, New York, USAVerified email at google.com
Alexander ZiminApplied Scientist at AmazonVerified email at ist.ac.at
Rémi MunosDeepMindVerified email at inria.fr
Joan Bas-SerranoPhD student, Universitat Pompeu FabraVerified email at upf.edu
Alessandro LazaricResearch Scientist, Facebook Artificial Intelligence ResearchVerified email at inria.fr

Gergely Neu

Artificial Intelligence and Machine Learning group, Universitat Pompeu Fabra

Verified email at upf.edu - Homepage

machine learning online learning learning theory reinforcement learning


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Apprenticeship learning using inverse reinforcement learning and gradient methods G Neu, C Szepesvári Proc. UAI, 295-302, 2007	267*	2007
A unified view of entropy-regularized Markov decision processes G Neu, A Jonsson, V Gómez arXiv preprint arXiv:1705.07798, 2017	162	2017
Online Markov decision processes under bandit feedback G Neu, A Antos, A György, C Szepesvári Advances in Neural Information Processing Systems 23, 2010	160	2010
Boltzmann Exploration Done Right N Cesa-Bianchi, C Gentile, G Lugosi, G Neu Neural Information Processing Systems (NIPS), 6287-6296, 2017	109	2017
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search A Zimin, G Neu Neural Information Processing Systems (NIPS), 2013	94	2013
Efficient learning by implicit exploration in bandit problems with side observations T Kocák, G Neu, M Valko, R Munos Neural Information Processing Systems (NIPS), 2014	93	2014
Explore no more: Improved high-probability regret bounds for non-stochastic bandits G Neu Neural Information Processing Systems (NIPS), 2015	86	2015
Training parsers by inverse reinforcement learning G Neu, C Szepesvári Machine learning 77 (2), 303-337, 2009	78	2009
An efficient algorithm for learning with semi-bandit feedback G Neu, G Bartók Algorithmic Learning Theory (ALT 2013), 2013	74	2013
The adversarial stochastic shortest path problem with unknown transition probabilities G Neu, A György, C Szepesvári AI & Statistics, 2012	71	2012
The online loop-free stochastic shortest-path problem G Neu, A György, C Szepesvári The 23rd Annual Conference on Learning Theory (COLT 2010), 2010	63	2010
Exploiting easy data in online optimization A Sani, G Neu, A Lazaric Neural Information Processing Systems (NIPS), 2014	54	2014
Algorithmic stability and hypothesis complexity T Liu, G Lugosi, G Neu, D Tao Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017	51	2017
Collaborative spatial reuse in wireless networks via selfish multi-armed bandits F Wilhelmi, C Cano, G Neu, B Bellalta, A Jonsson, S Barrachina-Muñoz Ad Hoc Networks 88, 129-141, 2019	43	2019
First-order regret bounds for combinatorial semi-bandits G Neu The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015	40	2015
Prediction by random-walk perturbation L Devroye, G Lugosi, G Neu The 26th Annual Conference on Learning Theory (COLT 2013), 460-473, 2013	40	2013
Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs F Wilhelmi, S Barrachina-Muñoz, B Bellalta, C Cano, A Jonsson, G Neu Journal of Network and Computer Applications 127, 26-42, 2019	35	2019
Iterate averaging as regularization for stochastic gradient descent G Neu, L Rosasco The 31st Annual Conference on Learning Theory (COLT 2018), 3222-3242, 2018	35	2018
Online learning with noisy side observations T Kocák, G Neu, M Valko International Conference on Artificial Intelligence and Statistics, 1186-1194, 2016	33	2016
Online combinatorial optimization with stochastic decision sets and adversarial losses G Neu, M Valko Neural Information Processing Systems (NIPS), 2014	28	2014

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