Stochastic Online Learning with Probabilistic Graph Feedback

Abstract

We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability p_ij. Two cases are covered. (a) The one-step case, where after playing arm i the learner observes a sample reward feedback of arm j with independent probability p_ij. (b) The cascade case where after playing arm i the learner observes feedback of all arms j in a probabilistic cascade starting from i – for each (i,j) with probability p_ij, if arm i is played or observed, then a reward sample of arm j would be observed with independent probability p_ij. Previous works mainly focus on deterministic graphs which corresponds to one-step case with p_ij ∈ {0,1}, an adversarial sequence of graphs with certain topology guarantees, or a specific type of random graphs. We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability.