Group-wise oracle-efficient algorithms for online multi-group learning
This work addresses fairness applications by enabling efficient learning over large, overlapping groups without explicit enumeration, though it is incremental as it builds on prior multi-group learning models.
The paper tackles the problem of online multi-group learning where groups are too large to enumerate, by designing oracle-efficient algorithms that achieve sublinear regret in i.i.d., adversarial smoothed, and adversarial transductive settings.
We study the problem of online multi-group learning, a learning model in which an online learner must simultaneously achieve small prediction regret on a large collection of (possibly overlapping) subsequences corresponding to a family of groups. Groups are subsets of the context space, and in fairness applications, they may correspond to subpopulations defined by expressive functions of demographic attributes. In contrast to previous work on this learning model, we consider scenarios in which the family of groups is too large to explicitly enumerate, and hence we seek algorithms that only access groups via an optimization oracle. In this paper, we design such oracle-efficient algorithms with sublinear regret under a variety of settings, including: (i) the i.i.d. setting, (ii) the adversarial setting with smoothed context distributions, and (iii) the adversarial transductive setting.