Publications

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Porco, A., Kaltenbrunner, A., and Gómez, V., Low-rank approximations for predicting voting behaviour, NIPS 2015 Workshop: Networks in the Social and Information Sciences, 2015.
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Gama, J., Camacho, R., Brazdil, P., Jorge, A., and Torgo, L., Machine Learning: ECML 2005, 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings, Lecture Notes in Computer Science, vol. 3720, 2005.
Jiménez, S., Fernández, F., and Borrajo, D., Machine Learning of Plan Robustness Knowledge About Instances, Machine Learning: {ECML} 2005, 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings, 2005, pp. 609–616.
Bäckström, C., Jonsson, A., and Jonsson, P., Macros, Reactive Plans and Compact Representations, Proceedings of the 20th European Conference on Artificial Intelligence (ECAI'12), 2012, pp. 85-90.
Dalmau, V., and Krokhin, A. A., Majority constraints have bounded pathwidth duality, Eur. J. Comb., vol. 29, 2008, pp. 821-837.
Dalmau, V., and Larose, B., Maltsev + Datalog –$>$ Symmetric Datalog, LICS, 2008, pp. 297-306.
Aragón, P., Gómez, V., and Kaltenbrunner, A., Measuring Platform Effects in Digital Democracy, The Internet, Policy & Politics Conference (IPPC), 2016. measuring-platform-effects.pdf (891.2 KB)
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.
Albore, A., Alechina, N., Bertoli, P., Ghidini, C., Logan, B., and Serafini, L., Model-Cheching Memory Requirements Of Resource-Bounded Reasoners, Proc. of 21st National Conference on Artificial Intelligence (AAAI-06), Boston, Massachusetts: 2006. Paper AAAI (522.37 KB)
Francès, G., and Geffner, H., Modeling and Computation in Planning: Better Heuristics from More Expressive Languages, 25th International Conference on Automated Planning and Scheduling (ICAPS 2015), 2015.
Kominis, F., and Geffner, H., Multiagent Online Planning with Nested Beliefs and Dialogue, Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS'17), 2017.
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György, A., and Neu, G., Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding, Proceedings of the {IEEE} International Symposium on Information Theory ({ISIT} 2011), 2011.
György, A., and Neu, G., Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding, IEEE Transactions on Information Theory, vol. 60, 2014, pp. 2823–2834.
Haslum, P., Bonet, B., and Geffner, H., New admissible heuristics for optimal planning, Proc. AAAI-05, 2005.
Aguilera, S., Jiménez, S., Bolaños, D., Torre, M. - S., and Colás, J., A New Mobile Text Telephony System Based on GPRS Communications, Computers Helping People with Special Needs, 9th International Conference, {ICCHP} 2004, Paris, France, July 7-9, 2004, Proceedings, 2004, pp. 1160–1166.
Dalmau, V., A new tractable class of constraint satisfaction problems, Ann. Math. Artif. Intell., vol. 44, 2005, pp. 61-85.
Dalmau, V., A New Tractable Class of Constraint Satisfaction Problems, AMAI, 2000.
Geffner, H., Non-classical Planning with a Classical Planner: The Power of Transformations, Logics in Artificial Intelligence, Springer International Publishing, 2014, pp. 33–47.
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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.
Zimin, A., and Neu, G., Online learning in episodic Markovian decision processes by relative entropy policy search, Advances in Neural Information Processing Systems 26, 2013, pp. 1583–1591.
Kocák, T., Neu, G., and Valko, M., Online learning with Erdős-Rényi side-observation graphs, Uncertainty in Artificial Intelligence, 2016, pp. 339–347.
Kocák, T., Neu, G., and Valko, M., Online learning with noisy side observations, International Conference on Artificial Intelligence and Statistics, 2016, pp. 1186–1194.
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.
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., 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.

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