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Preprints
- J. Bas-Serrano and G. Neu: Faster saddle-point optimization for solving large-scale Markov decision processes. Under review.
- N. Mücke, G. Neu and L. Rosasco: Beating SGD saturation with tail-averaging and minibatching. To appear in Advances in Neural Information
Processing Systems 32
(NIPS), 2019.
- C. Riquelme, H. Penedones, D. Vincent, H. Maennel, S. Gelly, T. Mann, A. Barreto and G. Neu: Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. To appear in Advances in Neural Information
Processing Systems 32
(NIPS), 2019.
- G. Neu, A. Jonsson and V. Gómez: A unified view of entropy-regularized Markov decision processes. Under review. [poster] [slides]
- G. Lugosi, M. G. Markakis, G. Neu: On the hardness of inventory management with censored demand data. Under review.
Journal papers
- G. Neu and G. Bartók: Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits. In Journal on Machine Learning Research (JMLR), vol. 17(154), pp. 1-21, 2016.
- L. Devroye, G. Lugosi and G. Neu: Random-Walk
Perturbations for Online Combinatorial Optimization. In IEEE Transactions on Information Theory,
vol. 61, pp. 4099-4106, 2015.
- G. Neu, A.
György, Cs. Szepesvári and A. Antos: Online Markov
Decision
Processes under Bandit Feedback. In IEEE Transactions on Automatic
Control, vol. 59., pp. 676-691, 2014.
- A.
György and G.
Neu: Near-Optimal
Rates for Limited-Delay Universal Lossy Source
Coding. In IEEE Transactions on Information Theory, vol. 60, pp.
2823-2834, 2014.
- G. Neu and
Cs.
Szepesvári: Training
Parsers by Inverse Reinforcement Learning. In Machine
Learning, vol. 77(2), pp. 303-337, 2009.
Refereed conference papers
- W. Kotłowski and G. Neu: Bandit Principal Component Analysis. In Proceedings of the 32nd Annual Conference on Learning Theory (COLT), pp. 1994-2024, 2019. [slides]
- G. Lugosi, J. Olkhovskaya and G. Neu: Online influence maximization with local observations. In Proceedings of the 30th International Conference
on
Algorithmic Learning Theory (ALT), pp. 557-580, 2019.
- G. Neu and L. Rosasco: Iterate averaging as regularization for stochastic gradient descent. In Proceedings of the 31st Annual Conference on Learning Theory (COLT), pp. 3222-3242, 2018.
- N. Cesa-Bianchi, C. Gentile, G. Lugosi and G. Neu: Boltzmann exploration done right. In Advances in Neural Information
Processing Systems 30
(NIPS), pp. 6284-6293, 2017. [poster]
- G. Neu and V. Gómez: Fast rates for online learning in Linearly Solvable Markov Decision Processes. In Proceedings of the 30th Annual Conference on Learning Theory (COLT), pp. 1567-1588, 2017. [slides]
- T. Liu, G. Lugosi, G. Neu and D. Tao: Algorithmic stability and hypothesis complexity. In Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 2159-2167, 2017.
- T. Kocák, G. Neu and M. Valko: Online learning with Erdős-Rényi side-observation graphs. In Proceedings of
the 32nd Conference on
Uncertainty in Artificial Intelligence (UAI), pp. 339-346, 2016.
- T. Kocák, G. Neu and M. Valko: Online learning with noisy side observations. In Proceedings of
the Nineteenth International Conference on Artificial Intelligence and
Statistics (AISTATS), pp. 1186-1194, 2016.
- G. Neu: Explore no more: Improved high-probability regret bounds for non-stochastic bandits. In Advances in Neural Information
Processing Systems
28
(NIPS), pp. 3150-3158, 2015. [poster] [slides]
- G. Neu: First-order regret bounds for combinatorial semi-bandits. In Proceedings of the 28th Annual Conference on Learning Theory (COLT),
pp. 1360-1375, 2015.[poster] [slides]
- G. Neu and M. Valko: Online Combinatorial
Optimization with Stochastic Decision Sets and Adversarial Losses.
In Advances in Neural Information Processing Systems
27
(NIPS), pp. 2780-2788, 2014. [poster] [slides]
- T. Kocák, G.
Neu, M. Valko and R. Munos: Efficient Learning
by Implicit Exploration
in Bandit Problems with Side Observations. In Advances in Neural
Information Processing Systems
27
(NIPS), pp. 613-621, 2014. [poster] [slides]
- A. Sani, G. Neu and A. Lazaric: Exploiting Easy Data
in Online Optimization. In Advances in Neural Information
Processing Systems
27
(NIPS), pp. 810-818, 2014. [poster] [spotlight] [talk]
- A. Zimin and G.
Neu: Online
Learning in Episodic Markov Decision Processes by Relative Entropy
Policy Search. In Advances in Neural Information Processing Systems
26
(NIPS), pp. 1583-1591, 2013. [poster] [slides]
- G. Neu and G.
Bartók: An
Efficient Algorithm for Learning with Semi-Bandit
Feedback. In Proceedings of the 24th International Conference
on
Algorithmic Learning Theory (ALT), pp. 234-248, 2013. [poster] [slides] Full version in JMLR '16.
- L. Devroye, G.
Lugosi and G. Neu: Prediction
by Random-Walk Perturbation. In
Proceedings of the 26th Annual Conference on Learning Theory (COLT),
pp. 460-473, 2013. [slides] Full version in IEEE T-IT '15.
- G. Neu, A.
György, and Cs. Szepesvári: The Adversarial
Stochastic Shortest Path
Problem with Unknown Transition Probabilities. In Proceedings of
the
Fifteenth International Conference on Artificial Intelligence and
Statistics (AISTATS), pp. 805-813, 2012. [supplement] [poster]
- A. György and G.
Neu: Near-Optimal
Rates
for Limited-Delay Universal Lossy Source Coding. In 2011 IEEE
International Symposium
on
Information Theory, pp. 2218-2222, 2011.Full version in IEEE T-IT '14.
- G. Neu, A.
György, Cs. Szepesvári and A. Antos: Online Markov
Decision
Processes under Bandit Feedback. In Advances in Neural Information
Processing Systems 23 (NIPS), pp. 1804-1812, 2010. [poster] [spotlight] Full version in IEEE TAC '14.
- G. Neu, A.
György, and Cs. Szepesvári: The Online Loop-free
Stochastic
Shortest-Path Problem. In Proceedings of The 23rd Conference on
Learning Theory (COLT), pp. 231-243, 2010.
- G. Neu and Cs.
Szepesvári: Apprenticeship
Learning using Inverse Reinforcement
Learning and Gradient Methods.
In Proceedings of the 23rd
Conference on
Uncertainty in Artificial Intelligence (UAI), pp. 295-302, 2007.
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