BOLD

The project "Beyond Online Learning for better Decision making" (BOLD) is at the junction of learning theory, statistics, optimisation and game theory. Starting from the fact that underlying hypothesis on existing online learning are not satisfied in practice, it is possible to identify several barriers to a massive implementation of these techniques - the classical "one step, one decision, one reward" paradigm is unfit. - Optimality of algorithms is defined with respect to worst-case generic lower bounds - Algorithms were designed in a non-strategic or interactive environment. The objectives of the project are therefore to get rid of these barriers that prevent a larger development of online learning 1) Go beyond "one data, one decision" by noticing that the general idea that data must be treated on the fly one after the other one is not compulsory. Quite often, it is possible to group them, to improve the quality of each decision taken (maybe at the cost of a smaller number of decisions - and rewards) 2) Go beyond "one data, one reward". The concept of minimization of cumulated loss is useful in theory, but not adapted for practical applications. Many learning or optimisation objectives are defined globally, as a non-linear functions of the sequence of decisions. 3) Use the existing underlying structure of the data to "beat the lower bounds". Data generating processes are not necessarily the worst ones that theory consider to evaluate the performances of algorithms. They have some specific particularities, through existing correlations (or similarities) between the data, that are known a-priori. Using them will accelerate rates of convergence, the learning phase and the overall performance of algorithms. 4) Incorporate other interacting and learning agents in the environment. Relevant problems now involve not only one learning agent, but a network of inter-acting agents with conflictual objectives. Investigating learning algorithms in a game theoretic framework is therefore highly needed. 5) Model and study application driven frameworks. Instead of generalizing arbitraritly, and sometimes in seemingly random directions, the already existing models, the project BOLD will question their foundation and the correctness of them, in order to propose new models in line with the applications in mind.