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» Reinforcement Learning: An Introduction
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178
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KDD
2010
ACM
289views Data Mining» more  KDD 2010»
15 years 21 days ago
Exploitation and exploration in a performance based contextual advertising system
The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of on...
Wei Li 0010, Xuerui Wang, Ruofei Zhang, Ying Cui, ...
161
Voted
EMNLP
2011
14 years 2 months ago
Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation
We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and...
Ashish Venugopal, Jakob Uszkoreit, David Talbot, F...
126
Voted
AGENTS
2000
Springer
15 years 7 months ago
Adaptivity in agent-based routing for data networks
Adaptivity, both of the individual agents and of the interaction structure among the agents, seems indispensable for scaling up multi-agent systems MAS's in noisy environme...
David Wolpert, Sergey Kirshner, Christopher J. Mer...
118
Voted
ATAL
2008
Springer
15 years 4 months ago
On the usefulness of opponent modeling: the Kuhn Poker case study
The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state inform...
Alessandro Lazaric, Mario Quaresimale, Marcello Re...
128
Voted
NIPS
1993
15 years 4 months ago
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms,includ...
Tommi Jaakkola, Michael I. Jordan, Satinder P. Sin...