Publications

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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., 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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Ognibene, D., Pezzulo, G., and Baldassare, G., How Are Representations Affected by Scene Statistics in an Adaptive Active Vision System?, Epirob 2009, 2009.
Ognibene, D., Chinellato, E., Sarabia, M., and Demiris, Y., Contextual action recognition and target localization with an active allocation of attention on a humanoid robot, Bioinspiration & Biomimetics, vol. 8, 2013, p. 035002.
Ognibene, D., Rega, A., and Baldassarre, G., A model of reaching that integrates reinforcement learning and population encoding of postures, From Animals to Animats 9: Proceedings of the Ninth International Conference on the Simulation of Adaptive Behavior (SAB2006), Springer Berlin/Heidelberg, 2006, pp. 381–393.
Ognibene, D., and Baldassarre, G., Ecological Active Vision: Four Bio-Inspired Principles to Integrate Bottom-Up and Adaptive Top-Down Attention Tested With a Simple Camera-Arm Robot, Autonomous Mental Development, IEEE Transactions on, 2014.
Ognibene, D., Mannella, F., Pezzulo, G., and Baldassarre, G., Integrating reinforcement-learning, accumulator models, and motor-primitives to study action selection and reaching in monkeys, Proceedings of the 7th International Conference on Cognitive Modelling-ICCM06, 2006, pp. 214–219.
Ognibene, D., Chinellato, E., Sarabia, M., and Demiris, Y., Towards Contextual Action Recognition and Target Localization with Active Allocation of Attention, First International Conference, Living Machines 2012, Barcelona, Spain, July 9-12, 2012. Proceedings, Springer Berlin/Heidelberg, 2012, pp. 192–203.
Ognibene, D., Pezzulo, G., and Dindo, H., Resources allocation in a Bayesian, schema-based model of distributed action control., NIPS-Workshop on Probabilistic Approaches for Robotics and Control. (Poster) Vancouver, B.C., Canada, December 11, 2009, 2009.
Ognibene, D., and Baldassarre, G., APPRENDIMENTO PER RINFORZO E CODIFICA TRAMITE POPOLAZIONE NEURALE: UN MODELLO PER IL REACHING APPLICATO A DUE TASK, WIVA3 - 3rd Workshop Italiano Vita Artificial, Siena, Italy: 2006.
Ognibene, D., and Giglia, G., Use of hierarchical Bayesian framework in MTS studies to model different causes and novel possible forms of acquired MTS, Cognitive neuroscience, vol. 6, 2015, pp. 144–145.
Ognibene, D., Volpi, N. Catenacci, Pezzulo, G., and Baldassarre, G., Learning Epistemic Actions in Model-Free Memory-Free Reinforcement Learning: experiments with a neuro-robotic model, Living Machine 2013, SPRINGER-VERLAG BERLIN, 2013.
Ognibene, D., Pezzulo, G., and Baldassarre, G., How can bottom-up information shape learning of top-down attention-control skills?, Development and Learning (ICDL), 2010 IEEE 9th International Conference on, IEEE, 2010, pp. 231–237.
Ognibene, D., Ecological Adaptive Perception from a Neuro-Robotic perspective: theory, architecture and experiments., University of Genoa, 2009.
Ognibene, D., and Demiris, Y., Towards active event recognition, The 23rd International Joint Conference of Artificial Intelligence (IJCAI13), 2013.
Ognibene, D., Wu, Y., Lee, K., and Demiris, Y., Hierarchies for embodied action perception, Computational and Robotic Models of the Hierarchical Organization of Behavior, Springer, 2013, p. Chapter–5.

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