Gergely Neu
Gergely Neu
Artificial Intelligence and Machine Learning group, Universitat Pompeu Fabra
E-mail confirmado em upf.edu - Página inicial
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Apprenticeship learning using inverse reinforcement learning and gradient methods
G Neu, C Szepesvári
Proc. UAI, 295-302, 2007
260*2007
A unified view of entropy-regularized Markov decision processes
G Neu, A Jonsson, V Gómez
arXiv preprint arXiv:1705.07798, 2017
1422017
Online Markov decision processes under bandit feedback
G Neu, A György, C Szepesvári, A Antos
Neural Information Processing Systems (NIPS), 2010
117*2010
Boltzmann Exploration Done Right
N Cesa-Bianchi, C Gentile, G Lugosi, G Neu
Neural Information Processing Systems (NIPS), 6287-6296, 2017
872017
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search
A Zimin, G Neu
Neural Information Processing Systems (NIPS), 2013
832013
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
822014
Training parsers by inverse reinforcement learning
G Neu, C Szepesvári
Machine learning 77 (2), 303-337, 2009
782009
An efficient algorithm for learning with semi-bandit feedback
G Neu, G Bartók
Algorithmic Learning Theory (ALT 2013), 2013
732013
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
G Neu
Neural Information Processing Systems (NIPS), 2015
682015
The adversarial stochastic shortest path problem with unknown transition probabilities
G Neu, A György, C Szepesvári
AI & Statistics, 2012
682012
The online loop-free stochastic shortest-path problem
G Neu, A György, C Szepesvári
The 23rd Annual Conference on Learning Theory (COLT 2010), 2010
602010
Online Markov decision processes under bandit feedback
G Neu, A György, C Szepesvari, A Antos
IEEE Transactions on Automatic Control 59 (3), 676-691, 2013
542013
Exploiting easy data in online optimization
A Sani, G Neu, A Lazaric
Neural Information Processing Systems (NIPS), 2014
492014
Algorithmic stability and hypothesis complexity
T Liu, G Lugosi, G Neu, D Tao
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
452017
Prediction by random-walk perturbation
L Devroye, G Lugosi, G Neu
The 26th Annual Conference on Learning Theory (COLT 2013), 460-473, 2013
412013
First-order regret bounds for combinatorial semi-bandits
G Neu
The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015
372015
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
352019
Online learning with noisy side observations
T Kocák, G Neu, M Valko
International Conference on Artificial Intelligence and Statistics, 1186-1194, 2016
292016
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
262019
Iterate averaging as regularization for stochastic gradient descent
G Neu, L Rosasco
The 31st Annual Conference on Learning Theory (COLT 2018), 3222-3242, 2018
262018
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