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 google.com
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
Julia OlkhovskayaVrije Universiteit AmsterdamVerified email at vu.nl
Tomáš KocákUniversity of PotsdamVerified email at uni-potsdam.de
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-MuñozSenior Researcher at CTTCVerified email at cttc.cat
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
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
Luc DevroyeMcGill UniversityVerified email at cs.mcgill.ca
Rémi MunosDeepMindVerified email at inria.fr
Joan Bas-SerranoPhD student, Universitat Pompeu FabraVerified email at upf.edu
Sean MeynProfessor of ECE and Robert C. Pittman Eminent Scholar ChairVerified email at ece.ufl.edu

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Apprenticeship learning using inverse reinforcement learning and gradient methods G Neu, C Szepesvári Proc. UAI, 295-302, 2007	296*	2007
A unified view of entropy-regularized Markov decision processes G Neu, A Jonsson, V Gómez arXiv preprint arXiv:1705.07798, 2017	213	2017
Online Markov decision processes under bandit feedback G Neu, A Antos, A György, C Szepesvári Advances in Neural Information Processing Systems 23, 2010	184	2010
Boltzmann Exploration Done Right N Cesa-Bianchi, C Gentile, G Lugosi, G Neu Neural Information Processing Systems (NIPS), 6287-6296, 2017	148	2017
Explore no more: Improved high-probability regret bounds for non-stochastic bandits G Neu Neural Information Processing Systems (NIPS), 2015	118	2015
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search A Zimin, G Neu Neural Information Processing Systems (NIPS), 2013	112	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	110	2014
Training parsers by inverse reinforcement learning G Neu, C Szepesvári Machine learning 77 (2), 303-337, 2009	88	2009
An efficient algorithm for learning with semi-bandit feedback G Neu, G Bartók Algorithmic Learning Theory (ALT 2013), 2013	81	2013
The adversarial stochastic shortest path problem with unknown transition probabilities G Neu, A György, C Szepesvári AI & Statistics, 2012	78	2012
Algorithmic stability and hypothesis complexity T Liu, G Lugosi, G Neu, D Tao Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017	69	2017
The online loop-free stochastic shortest-path problem G Neu, A György, C Szepesvári The 23rd Annual Conference on Learning Theory (COLT 2010), 2010	68	2010
Exploiting easy data in online optimization A Sani, G Neu, A Lazaric Neural Information Processing Systems (NIPS), 2014	57	2014
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	55	2019
First-order regret bounds for combinatorial semi-bandits G Neu The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015	48	2015
A unifying view of optimism in episodic reinforcement learning G Neu, C Pike-Burke Advances in Neural Information Processing Systems 33, 2020	45	2020
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	45	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	44	2018
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent G Neu, GK Dziugaite, M Haghifam, DM Roy The 34th Annual Conference on Learning Theory (COLT 2020), 3526-3545, 2021	39	2021
Prediction by random-walk perturbation L Devroye, G Lugosi, G Neu The 26th Annual Conference on Learning Theory (COLT 2013), 460-473, 2013	39	2013

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