Gergely Neu

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Gabor LugosiICREA and Universitat Pompeu FabraCorreu electrònic verificat a upf.edu
Csaba SzepesvariDeepMind & University of AlbertaCorreu electrònic verificat a cs.ualberta.ca
Anders JonssonArtificial Intelligence and Machine Learning group, Universitat Pompeu FabraCorreu electrònic verificat a upf.edu
Julia OlkhovskayaTU DelftCorreu electrònic verificat a tudelft.nl
Andras GyorgyDeepMindCorreu electrònic verificat a google.com
Michal ValkoChief Models Officer @ Stealth Startup, Inria & MVA - Ex: Llama at Meta; Gemini and BYOL @ DeepmindCorreu electrònic verificat a meta.com
Cristina CanoAssociate Professor, WINE Group, Universitat Oberta de CatalunyaCorreu electrònic verificat a uoc.edu
Tomáš KocákUniversity of PotsdamCorreu electrònic verificat a uni-potsdam.de
Sergio Barrachina-MuñozSenior Researcher at CTTCCorreu electrònic verificat a cttc.cat
Vicenç GómezArtificial Intelligence and Machine Learning research group. Universitat Pompeu Fabra.Correu electrònic verificat a upf.edu
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di Milano and Politecnico di MilanoCorreu electrònic verificat a unimi.it
Matteo PapiniPolitecnico di MilanoCorreu electrònic verificat a polimi.it
Sean MeynProfessor of ECE and Robert C. Pittman Eminent Scholar ChairCorreu electrònic verificat a ece.ufl.edu
Gábor BartókETH ZurichCorreu electrònic verificat a ualberta.ca
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaCorreu electrònic verificat a unige.it
Claudio GentileGoogle Research, New York, USACorreu electrònic verificat a google.com
András AntosBudapest University of Technology and EconomicsCorreu electrònic verificat a cs.bme.hu
Prashant MehtaProfessor of Mechanical Science and Engineering, University of IllinoisCorreu electrònic verificat a illinois.edu
Fan LuUniversity of FloridaCorreu electrònic verificat a ufl.edu
Joan Bas-SerranoPhD student, Universitat Pompeu FabraCorreu electrònic verificat a upf.edu

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Gergely Neu

Artificial Intelligence and Machine Learning group, Universitat Pompeu Fabra

Correu electrònic verificat a upf.edu - Pàgina d'inici

machine learning online learning learning theory reinforcement learning


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Apprenticeship learning using inverse reinforcement learning and gradient methods G Neu, C Szepesvári Proc. UAI, 295-302, 2007	319*	2007
A unified view of entropy-regularized Markov decision processes G Neu, A Jonsson, V Gómez arXiv preprint arXiv:1705.07798, 2017	299	2017
Boltzmann Exploration Done Right N Cesa-Bianchi, C Gentile, G Lugosi, G Neu Neural Information Processing Systems (NeurIPS), 6287-6296, 2017	251	2017
Online Markov decision processes under bandit feedback G Neu, A Antos, A György, C Szepesvári Advances in Neural Information Processing Systems 23, 2010	225	2010
Explore no more: Improved high-probability regret bounds for non-stochastic bandits G Neu Neural Information Processing Systems (NIPS), 2015	211	2015
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search A Zimin, G Neu Neural Information Processing Systems (NIPS), 2013	161	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	141	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	105	2017
The adversarial stochastic shortest path problem with unknown transition probabilities G Neu, A György, C Szepesvári AI & Statistics, 2012	99	2012
Training parsers by inverse reinforcement learning G Neu, C Szepesvári Machine learning 77 (2), 303-337, 2009	99	2009
An efficient algorithm for learning with semi-bandit feedback G Neu, G Bartók Algorithmic Learning Theory (ALT 2013), 2013	93	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	92	2021
A unifying view of optimism in episodic reinforcement learning G Neu, C Pike-Burke Advances in Neural Information Processing Systems 33, 2020	85	2020
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
Iterate averaging as regularization for stochastic gradient descent G Neu, L Rosasco The 31st Annual Conference on Learning Theory (COLT 2018), 3222-3242, 2018	73	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	68	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	63	2019
First-order regret bounds for combinatorial semi-bandits G Neu The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015	60	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	58	2020

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