Gergely Neu 个人学术档案 - 学术资源搜索

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Gabor LugosiICREA and Universitat Pompeu Fabra在 upf.edu 的电子邮件经过验证
Csaba SzepesvariDeepMind & University of Alberta在 cs.ualberta.ca 的电子邮件经过验证
Anders JonssonArtificial Intelligence and Machine Learning group, Universitat Pompeu Fabra在 upf.edu 的电子邮件经过验证
Andras GyorgyDeepMind在 google.com 的电子邮件经过验证
Julia OlkhovskayaTU Delft在 tudelft.nl 的电子邮件经过验证
Michal ValkoLlama @ Meta Paris & Inria & MVA - Ex: Gemini and BYOL @ Google DeepMind在 meta.com 的电子邮件经过验证
Cristina CanoAssociate Professor, WINE Group, Universitat Oberta de Catalunya在 uoc.edu 的电子邮件经过验证
Tomáš KocákUniversity of Potsdam在 uni-potsdam.de 的电子邮件经过验证
Vicenç GómezArtificial Intelligence and Machine Learning research group. Universitat Pompeu Fabra.在 upf.edu 的电子邮件经过验证
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di Milano and Politecnico di Milano在 unimi.it 的电子邮件经过验证
Sergio Barrachina-MuñozSenior Researcher at CTTC在 cttc.cat 的电子邮件经过验证
Sean MeynProfessor of ECE and Robert C. Pittman Eminent Scholar Chair在 ece.ufl.edu 的电子邮件经过验证
Gábor BartókETH Zurich在 ualberta.ca 的电子邮件经过验证
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di Genova在 unige.it 的电子邮件经过验证
Matteo PapiniPolitecnico di Milano在 polimi.it 的电子邮件经过验证
Claudio GentileGoogle Research, New York, USA在 google.com 的电子邮件经过验证
András AntosBudapest University of Technology and Economics在 cs.bme.hu 的电子邮件经过验证
Prashant MehtaProfessor of Mechanical Science and Engineering, University of Illinois在 illinois.edu 的电子邮件经过验证
Fan LuUniversity of Florida在 ufl.edu 的电子邮件经过验证
Joan Bas-SerranoPhD student, Universitat Pompeu Fabra在 upf.edu 的电子邮件经过验证

关注

Gergely Neu

Artificial Intelligence and Machine Learning group, Universitat Pompeu Fabra

在 upf.edu 的电子邮件经过验证 - 首页

machine learning online learning learning theory reinforcement learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	269	2017
Boltzmann Exploration Done Right N Cesa-Bianchi, C Gentile, G Lugosi, G Neu Neural Information Processing Systems (NIPS), 6287-6296, 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	210	2010
Explore no more: Improved high-probability regret bounds for non-stochastic bandits G Neu Neural Information Processing Systems (NIPS), 2015	174	2015
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search A Zimin, G Neu Neural Information Processing Systems (NIPS), 2013	143	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	130	2014
Training parsers by inverse reinforcement learning G Neu, C Szepesvári Machine learning 77 (2), 303-337, 2009	94	2009
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	93	2017
The adversarial stochastic shortest path problem with unknown transition probabilities G Neu, A György, C Szepesvári AI & Statistics, 2012	93	2012
An efficient algorithm for learning with semi-bandit feedback G Neu, G Bartók Algorithmic Learning Theory (ALT 2013), 2013	91	2013
The online loop-free stochastic shortest-path problem G Neu, A György, C Szepesvári The 23rd Annual Conference on Learning Theory (COLT 2010), 2010	80	2010
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	76	2021
A unifying view of optimism in episodic reinforcement learning G Neu, C Pike-Burke Advances in Neural Information Processing Systems 33, 2020	70	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	67	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	62	2019
Exploiting easy data in online optimization A Sani, G Neu, A Lazaric Neural Information Processing Systems (NIPS), 2014	60	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	58	2019
First-order regret bounds for combinatorial semi-bandits G Neu The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015	56	2015
Logistic Q-Learning J Bas-Serrano, S Curi, A Krause, G Neu International Conference on Artificial Intelligence and Statistics, 3610-3618, 2021	50	2021

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