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COLT
2008
Springer
13 years 9 months ago
High-Probability Regret Bounds for Bandit Online Linear Optimization
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ( ...
Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, S...
ICALP
2009
Springer
14 years 8 months ago
Learning Halfspaces with Malicious Noise
We give new algorithms for learning halfspaces in the challenging malicious noise model, where an adversary may corrupt both the labels and the underlying distribution of examples....
Adam R. Klivans, Philip M. Long, Rocco A. Servedio
ALT
2005
Springer
14 years 4 months ago
Defensive Universal Learning with Experts
This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedba...
Jan Poland, Marcus Hutter
CSFW
2011
IEEE
12 years 7 months ago
Regret Minimizing Audits: A Learning-Theoretic Basis for Privacy Protection
Abstract—Audit mechanisms are essential for privacy protection in permissive access control regimes, such as in hospitals where denying legitimate access requests can adversely a...
Jeremiah Blocki, Nicolas Christin, Anupam Datta, A...
ATAL
2008
Springer
13 years 9 months ago
No-regret learning and a mechanism for distributed multiagent planning
We develop a novel mechanism for coordinated, distributed multiagent planning. We consider problems stated as a collection of single-agent planning problems coupled by common soft...
Jan-P. Calliess, Geoffrey J. Gordon