Volgen
Gergely Neu
Gergely Neu
Artificial Intelligence and Machine Learning group, Universitat Pompeu Fabra
Geverifieerd e-mailadres voor upf.edu - Homepage
Titel
Geciteerd door
Geciteerd door
Jaar
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
2802017
Boltzmann Exploration Done Right
N Cesa-Bianchi, C Gentile, G Lugosi, G Neu
Neural Information Processing Systems (NIPS), 6287-6296, 2017
2282017
Online Markov decision processes under bandit feedback
G Neu, A Antos, A György, C Szepesvári
Advances in Neural Information Processing Systems 23, 2010
2202010
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
G Neu
Neural Information Processing Systems (NIPS), 2015
1912015
Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search
A Zimin, G Neu
Neural Information Processing Systems (NIPS), 2013
1522013
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
1312014
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
1002017
The adversarial stochastic shortest path problem with unknown transition probabilities
G Neu, A György, C Szepesvári
AI & Statistics, 2012
962012
Training parsers by inverse reinforcement learning
G Neu, C Szepesvári
Machine learning 77 (2), 303-337, 2009
962009
An efficient algorithm for learning with semi-bandit feedback
G Neu, G Bartók
Algorithmic Learning Theory (ALT 2013), 2013
932013
The online loop-free stochastic shortest-path problem
G Neu, A György, C Szepesvári
The 23rd Annual Conference on Learning Theory (COLT 2010), 2010
832010
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
812021
A unifying view of optimism in episodic reinforcement learning
G Neu, C Pike-Burke
Advances in Neural Information Processing Systems 33, 2020
772020
Iterate averaging as regularization for stochastic gradient descent
G Neu, L Rosasco
The 31st Annual Conference on Learning Theory (COLT 2018), 3222-3242, 2018
702018
Exploiting easy data in online optimization
A Sani, G Neu, A Lazaric
Neural Information Processing Systems (NIPS), 2014
642014
Collaborative spatial reuse in wireless networks via selfish multi-armed bandits
F Wilhelmi, C Cano, G Neu, B Bellalta, A Jonsson, S Barrachina-Muńoz
Ad Hoc Networks 88, 129-141, 2019
632019
Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs
F Wilhelmi, S Barrachina-Muńoz, B Bellalta, C Cano, A Jonsson, G Neu
Journal of Network and Computer Applications 127, 26-42, 2019
592019
First-order regret bounds for combinatorial semi-bandits
G Neu
The 28th Annual Conference on Learning Theory (COLT 2015), 1360–1375, 2015
572015
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
522020
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20