Fair Regret Minimization Queries
Y Ma, J Zheng�- …�Conference on Intelligent Data Engineering and�…, 2021 - Springer
… 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 …
generalize the k-regret query to its fair form, ie, the fair regret minimization query. Moreover, we …
Strongly truthful interactive regret minimization
… 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 …
performance for all interactive algorithms in the rest of the experiments. We proceed with the …
Collaborative Bayesian optimization with fair regret
… 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…
algorithm whose convergence rate can be theoretically guaranteed by bounding the new fair regret…
Reverse regret query
… 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 …
is minimized. There are many … To maintain consistency and enable a fair comparison of our …
Pseudonorm Approachability and Applications to Regret Minimization
… 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 …
Keywords: Blackwell’s approachability, … Of course, this comparison is not completely fair: in both …
[PDF][PDF] A unified optimization algorithm for solving" regret-minimizing representative" problems
… 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 …
finding the set of size k that minimizes the average regret ratio over all linear functions. Prior …
A fully dynamic algorithm for k-regret minimizing sets
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 …
applications such as multi-criteria decision making, web search, and recommendation. The k-regret …
Continuous -Regret Minimization Queries: A Dynamic Coreset Approach
… 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 …
FD-RMS and DYNCORE. Datasets. We performed our experiments on one synthetic and four …
The cost of a reductions approach to private fair optimization
D Alabi�- arXiv preprint arXiv:1906.09613, 2019 - arxiv.org
… 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, …
model for … We resolve a few open questions and show applicability to fair machine learning, …
Efficient computation of regret-ratio minimizing set: A compact maxima representative
… 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 …
Performance Measures: For the … Specifically, to be fair to the sweeping-line algorithm, we …