Pareto Front Identification from Stochastic Bandit Feedback


Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:939-947, 2016.


We consider the problem of identifying the Pareto front for multiple objectives from a finite set of operating points. Sampling an operating point gives a random vector where each coordinate corresponds to the value of one of the objectives. The Pareto front is the set of operating points that are not dominated by any other operating point in respect to all objectives (considering the mean of their objective values). We propose a confidence bound algorithm to approximate the Pareto front, and prove problem specific lower and upper bounds, showing that the sample complexity is characterized by some natural geometric properties of the operating points. Experiments confirm the reliability of our algorithm. For the problem of finding a sparse cover of the Pareto front, we propose an asymmetric covering algorithm of independent interest.

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