The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel it...
Seung-Jean Kim, Argyrios Zymnis, Alessandro Magnan...
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a wide variety of r...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
Literature on the use of machine learning (ML) algorithms for classifying IP traffic has relied on fullflows or the first few packets of flows. In contrast, many real-world scenar...
The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...