Weak hypotheses and boosting for generic object detection and recognition

Opelt, A., Fussenegger, M., Pinz, A. & Auer, P., 2004, Computer Vision - ECCV 2004, 8th European Conference on Computer Vision. S. 71-84

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Visual Classification of Images by Learning Geometric Appearances Through Boosting

Antenreiter, M., Savu-Krohn, C. & Auer, P., 2006, IAPR Workshop, ANNPR. Springer, S. 233-243

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Variational Regret Bounds for Reinforcement Learning

Ortner, R., Gajane, P. & Auer, P., 2019, Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, UAI 2019. S. 81-90

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Using Upper Confidence Bounds for Online Learning

Auer, P., 2000, 41th Annual Symposium on Foundations of Computer Science. S. 270-293

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Using a spatio-temporal reasoning system to improve object models on the fly

Antenreiter, M., Prankl, J., Vincze, M. & Auer, P., 2009, 33rd Workshop of the Austrian Association for Pattern Recognition - Visual Learning. S. 25-36

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Upper-confidence-bound algorithms for active learning in multi-armed bandits

Carpentier, A., Lazaric, A., Ghavamzadeh, M., Munos, R. & Auer, P., 20 Okt. 2011, Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Proceedings. S. 189-203 15 S. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 6925 LNAI).

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Upper-Confidence-Bound Algorithms for Active Learning in Mulit-armed Bandits

Auer, P., Carpentier, A., Lazaric, A., Ghavamzadeh, M. & Munos, R., 2011, The 22nd International Conference on Algorithmic Learning Theory. S. 189-203

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Unification in the Combination of Disjoint Theories

Auer, P., 1991, Unification in the Combination of Disjoint Theories. S. 177-186

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