• 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, 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.
  

• 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.

• 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.
  

• Y. Abbasi-Yadkori and G. Neu: Online Learning in MDPs with Side Information. Arxiv preprint, 2014.