Publications

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Neu, G., and Valko, M., Online combinatorial optimization with stochastic decision sets and adversarial losses, Advances in Neural Information Processing Systems 27 (NIPS), 2014, pp. 2780-2788.
Neu, G., György, A., Szepesvári, \textC., and Antos, A., Online Markov Decision Processes under Bandit Feedback, IEEE Transactions on Automatic Control, vol. 59, 2013, pp. 676–691.
Neu, G., and Gómez, V., Fast rates for online learning in Linearly Solvable Markov Decision Processes, Proceedings of the 30th Conference on Learning Theory (COLT), 2017, pp. 1567-1588.
Neu, G., and Szepesvári, \textC., Training parsers by inverse reinforcement learning, Machine Learning Journal, vol. 77, 2009, pp. 303–337.
Neu, G., First-order regret bounds for combinatorial semi-bandits, {Proceedings of the 27th Annual Conference on Learning Theory (COLT)}, 2015, pp. 1360-1375.
Neu, G., and Bartók, G., An Efficient Algorithm for Learning with Semi-Bandit Feedback, Proceedings of the 24th International Conference on Algorithmic Learning Theory (ALT), 2013, pp. 234-248.
Neu, G., and Bartók, G., Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits, Journal of Machine Learning Research, vol. 17, 2016, pp. 1-21.
Neu, G., György, A., Szepesvári, \relaxCs., and Antos, A., Online Markov Decision Processes under Bandit Feedback, Advances in Neural Information Processing Systems 23 (NIPS), 2010, pp. 1804–1812.
Neu, G., Explore no more: Improved high-probability regret bounds for non-stochastic bandits, Advances in Neural Information Processing Systems 28 (NIPS), 2015.
Neu, G., Gómez, V., and Jonsson, A., A unified view of entropy-regularized Markov decision processes, Proceedings of the Deep Reinforcement Learning Symposium at NIPS 2017, 2017.
Neu, G., György, A., and Szepesvári, \textC., The adversarial stochastic shortest path problem with unknown transition probabilities, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, N. Lawrence and Girolami, M., 2012, pp. 805–813.
Neu, G., György, A., and Szepesvári, \relaxCs., The Online Loop-free Stochastic Shortest-Path Problem, {Proceedings of the 23rd Annual Conference on Learning Theory (COLT)}, 2010, pp. 231–243.
Nivel, E., Thórisson, K. R., Steunebrink, B. R., Dindo, H., Pezzulo, G., Rodrıguez, M., Hernández, C., Ognibene, D., Schmidhuber, J., Sanz, R., and , Bounded Seed-AGI, AGI 2014: Quebec City, QC, Canada, Springer Berlin/Heidelberg, 2014, pp. 85–96.
Nivel, E., Thórisson, K. R., Steunebrink, B. R., Dindo, H., Pezzulo, G., Rodriguez, M., Hernandez, C., Ognibene, D., Schmidhuber, J., Sanz, R., and , Bounded Recursive Self-Improvement, arXiv preprint arXiv:1312.6764, 2013.
Nivel, E., risson, K. R. Þó, Thórisson, K. R., Dindo, H., Pezzulo, G., Rodriguez, M., Corbato, C., Steunebrink, B., Ognibene, D., Chella, A., and , Autocatalytic endogenous reflective architecture, 2013.
Nivel, E., Thórisson, K. R., Steunebrink, B. R., Dindo, H., Pezzulo, G., Rodrıguez, M., Hernández, C., Ognibene, D., Schmidhuber, J., Sanz, R., and , AUTONOMOUS ACQUISITION OF NATURAL LANGUAGE, IADIS International Conference on Intelligent Systems & Agents, 2014, pp. 58–66.
Nunes, C., Jonsson, A., Camara, O., and Bijnens, B., A Decision Tree Approach for Imprecise Data, Proceedings of the 11th Women in Machine Learning Workshop (WiML'16), 2016.

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