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
Anders JonssonArtificial Intelligence and Machine Learning group, Universitat Pompeu FabraVerified email at upf.edu
Julia OlkhovskayaTU DelftVerified email at tudelft.nl
Andras GyorgyDeepMindVerified email at google.com
Michal ValkoLlama @ Meta Paris & Inria & MVA - Ex: Gemini and BYOL @ Google DeepMindVerified email at meta.com
Cristina CanoAssociate Professor, WINE Group, Universitat Oberta de CatalunyaVerified email at uoc.edu
Tomáš KocákUniversity of PotsdamVerified email at uni-potsdam.de
Vicenç GómezArtificial Intelligence and Machine Learning research group. Universitat Pompeu Fabra.Verified email at upf.edu
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di Milano and Politecnico di MilanoVerified email at unimi.it
Sergio Barrachina-MuñozSenior Researcher at CTTCVerified email at cttc.cat
Sean MeynProfessor of ECE and Robert C. Pittman Eminent Scholar ChairVerified email at ece.ufl.edu
Matteo PapiniPolitecnico di MilanoVerified email at polimi.it
Gábor BartókETH ZurichVerified email at ualberta.ca
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it
Claudio GentileGoogle Research, New York, USAVerified email at google.com
András AntosBudapest University of Technology and EconomicsVerified email at cs.bme.hu
Prashant MehtaProfessor of Mechanical Science and Engineering, University of IllinoisVerified email at illinois.edu
Fan LuUniversity of FloridaVerified email at ufl.edu
Joan Bas-SerranoPhD student, Universitat Pompeu FabraVerified email at upf.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	321*	2007
A unified view of entropy-regularized Markov decision processes G Neu, A Jonsson, V Gómez arXiv preprint arXiv:1705.07798, 2017	289	2017
Boltzmann Exploration Done Right N Cesa-Bianchi, C Gentile, G Lugosi, G Neu Neural Information Processing Systems (NIPS), 6287-6296, 2017	234	2017
Online Markov decision processes under bandit feedback G Neu, A Antos, A György, C Szepesvári Advances in Neural Information Processing Systems 23, 2010	224	2010
Explore no more: Improved high-probability regret bounds for non-stochastic bandits G Neu Neural Information Processing Systems (NIPS), 2015	197	2015
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search A Zimin, G Neu Neural Information Processing Systems (NIPS), 2013	155	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	134	2014
Algorithmic stability and hypothesis complexity T Liu, G Lugosi, G Neu, D Tao Proceedings of the 34th International Conference on Machine Learning, 2159-2167, 2017	104	2017
The adversarial stochastic shortest path problem with unknown transition probabilities G Neu, A György, C Szepesvári AI & Statistics, 2012	97	2012
Training parsers by inverse reinforcement learning G Neu, C Szepesvári Machine learning 77 (2), 303-337, 2009	97	2009
An efficient algorithm for learning with semi-bandit feedback G Neu, G Bartók Algorithmic Learning Theory (ALT 2013), 2013	94	2013
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	88	2021
The online loop-free stochastic shortest-path problem G Neu, A György, C Szepesvári The 23rd Annual Conference on Learning Theory (COLT 2010), 2010	83	2010
A unifying view of optimism in episodic reinforcement learning G Neu, C Pike-Burke Advances in Neural Information Processing Systems 33, 2020	79	2020
Iterate averaging as regularization for stochastic gradient descent G Neu, L Rosasco The 31st Annual Conference on Learning Theory (COLT 2018), 3222-3242, 2018	72	2018
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	64	2019
Exploiting easy data in online optimization A Sani, G Neu, A Lazaric Neural Information Processing Systems (NIPS), 2014	64	2014
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	59	2019
First-order regret bounds for combinatorial semi-bandits G Neu The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015	57	2015
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits G Neu, J Olkhovskaya The 33rd Annual Conference on Learning Theory (COLT 2020), 3049-3068, 2020	56	2020

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