Departments and Chairs
Organisational unit: Departments and Institutes
Research output
- Published
Adaptive and self-confident online learning algorithms
Auer, P., Cesa-Bianchi, N. & Gentile, C., 2002, In: Journal of computer and system sciences (JCSS). 64, p. 48-78Research output: Contribution to journal › Article › Research › peer-review
- Published
Relevance Feedback Models for Content-Based Image Retrieval
Auer, P. & Leung, P., 2009, Multimedia Analysis, Processing and Communications.Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research
- Published
The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant
Auer, P., Kivinen, J. & Warmuth, M. K., 1997, In: Artificial intelligence. p. 325-343Research output: Contribution to journal › Article › Research › peer-review
- Published
On the Complexity of Function Learning
Auer, P., Long, P. M., Maass, W. & Wöginger, G. J., 1993, Sixth Annual ACM Conference on Computational Learning Theory (COLT 1993). p. 392-401Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Some thoughts on Boosting and Neural Networks
Auer, P., 1998, 3. Cottbuser Workshop "Aspekte des Neuronalen Lernens". p. 11-28Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Near-optimal Regret Bounds for Reinforcement Learning
Auer, P., Jaksch, T. & Ortner, R., 2009, Advances in neural information processing systems 21. MIT Press, p. 89-96Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
UCB Revisited: Improved Regret Bounds for the Stochastic Multi-Armed Bandit Problem
Auer, P. & Ortner, R., 2010, In: Periodica Mathematica Hungarica. 61, p. 55-65Research output: Contribution to journal › Article › Research › peer-review
- Published
Near-optimal Regret Bounds for Reinforcement Learning
Auer, P., Jaksch, T. & Ortner, R., 2008.Research output: Contribution to conference › Poster › Research › peer-review
- Published
A learning rule for very simple universal approximators consisting of a single layer of perceptrons
Auer, P., Burgsteiner, H. & Maass, W., 2008, In: Neural networks. 21, p. 786-795Research output: Contribution to journal › Article › Research › peer-review
- Published
Exploration and Exploitation in Online Learning
Auer, P., 2011, International Conference on Adaptive and Intelligent Symstems - ICAIS 2011. p. 2-2Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
