… However, existing regret-based approaches cannot answer the k-regret query on the … we
generalize the k-regret query to its fair form, ie, the fair regret minimization query. Moreover, we …

… To perform fair comparison, we set the default value of s to be 3 which gives reasonable
performance for all interactive algorithms in the rest of the experiments. We proceed with the …

… notion of instantaneous fair regret st by … fair (cumulative) regret and a collaborative BO
algorithm whose convergence rate can be theoretically guaranteed by bounding the new fair regret…

… The regret minimization query aims to find a representative … that in the whole product dataset
is minimized. There are many … To maintain consistency and enable a fair comparison of our …

… We argue that in many applications such as regret minimization, it … in regret minimization.
Keywords: Blackwell’s approachability, … Of course, this comparison is not completely fair: in both …

… minimizing set (RRMS), ie, the subset of a required size k that minimizes the maximum regret…
finding the set of size k that minimizes the average regret ratio over all linear functions. Prior …

Selecting a small set of representatives from a large database is important in many
applications such as multi-criteria decision making, web search, and recommendation. The k-regret …

… For fair comparison, we adjust the parameters so that the size of the d-net is the same in
FD-RMS and DYNCORE. Datasets. We performed our experiments on one synthetic and four …

… approach to fair optimization and learning where a black-box optimizer is used to learn a fair
model for … We resolve a few open questions and show applicability to fair machine learning, …

… Nonetheless, to be fair in comparing the algorithms’ … of applying them for regret minimization.
Performance Measures: For the … Specifically, to be fair to the sweeping-line algorithm, we …