On-line methods in machine learning

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The proposed research is focused on on-line learning and sequential decision making in stochastic environments. Examples of sequential decision making problems abound: controlling industrial plants, performing clinical trials, managing investments, monitoring pipeline-systems, etc. An on-line learning algorithm improves the performance of a system during its normal operation. In contrast, off-line learning algorithms are trained with a fixed amount of data and then used with learning turned off. In off-line learning the training data has to be fully representative of the system's behaviour or performance may be poor. On-line learning systems overcome this potential weakness because they never stop adapting.On-line learning is challenging since learning and control is interleaved. Current on-line learning solutions are limited to small finite domains or make strong assumptions (such as that the uncertainty is parametric) that seriously limit their applicability since complex environments do not meet this conditions.Here I propose to develop and study on-line learning algorithms that work in large, complex environments. I plan to concentrate on the following aspects: (i) Understanding what makes efficient on-line learning possible, (ii) characterizing the behaviour of on-line learning algorithms, (iii) developing on-line learning algorithms that are efficient in terms of both data and computation. In order to accomplish this goal, I analyse the performance of the algorithms theoretically, deriving bounds that show their strengths and reveal their weaknesses, followed by an effort to improve their weakest points.Although delivering good on-line performance in large-scale, realistic environments is an ambitious goal, progress on this area is likely to find significant future, real-world applications.